Artificial neural networks in medical diagnosis

Artificial neural networks: fundamentals, computing, design, and application

Basheer, I. A., & Hajmeer, M. N. (2000) · Journal of Microbiological Methods · doi:10.1016/s0167-7012(00)00201-3 · Cited by 3,234 · see citing works

Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performance

Mazurowski, M. A., Habas, P. A., Zurada, J. M., Lo, J. Y.-C., Baker, J. A., & Tourassi, G. D. (2007) · Neural Networks · doi:10.1016/j.neunet.2007.12.031 · Cited by 777 · see citing works

Neural networks in chemistry and drug design

Zupan, J., & Gasteiger, J. (1999) · Medical Entomology and Zoology · Cited by 724 · see citing works

An introduction to neural computing

(1990) · Biological Psychology · doi:10.1016/0301-0511(90)90019-s · Cited by 621 · see citing works

Artificial Neural Networks

Kubát, M. (2017) · Springer eBooks · doi:10.1007/978-3-319-63913-0_5 · Cited by 379 · see citing works

Artificial Intelligence in Medical Diagnosis

Szolovits, P., PATIL, R. S., & SCHWARTZ, W. B. (1988) · Annals of Internal Medicine · doi:10.7326/0003-4819-108-1-80 · Cited by 277 · see citing works

Feature selection with neural networks

Verikas, A., & Bačauskienė, M. (2002) · Pattern Recognition Letters · doi:10.1016/s0167-8655(02)00081-8 · Cited by 253 · see citing works

Development of a decision support system for diagnosis and grading of brain tumours using in vivo magnetic resonance single voxel spectra

Tate, A. R., Underwood, J., Acosta, D., Julià‐Sapé, M., Majós, C., Moreno‐Torres, À., Howe, F. A., van der Graaf, M., Lefournier, V., Murphy, M. M., et al. (2006) · NMR in Biomedicine · doi:10.1002/nbm.1016 · Cited by 227 · see citing works

Artificial neural networks for diagnosis and survival prediction in colon cancer

Ahmed, F. (2005) · Molecular Cancer · doi:10.1186/1476-4598-4-29 · Cited by 216 · see citing works

Segmentation of multiple sclerosis lesions in MR images: a review

Mortazavi, D., Kouzani, A. Z., & Hamid Soltanian-zadeh, A. (2011) · Neuroradiology · doi:10.1007/s00234-011-0886-7 · Cited by 146 · see citing works

A new approach for locating the minor apical foramen using an artificial neural network

Saghiri, M. A., Asgar, K., Boukani, K. K., Lotfi, M., Aghili, H., Delvarani, A., Karamifar, K., Saghiri, A. M., Mehrvarzfar, P., & Garcı́a-Godoy, F. (2011) · International Endodontic Journal · doi:10.1111/j.1365-2591.2011.01970.x · Cited by 143 · see citing works

Prognostic models for outcome following liver resection for colorectal cancer metastases: A systematic review

Spelt, L., Andersson, B., Nilsson, J., & Andersson, R. (2011) · European Journal of Surgical Oncology · doi:10.1016/j.ejso.2011.10.013 · Cited by 134 · see citing works

Tuberculosis Disease Diagnosis Using Artificial Neural Networks

Er, O., Temurtaş, F., & Tanrıkulu, A. Ç. (2008) · Journal of Medical Systems · doi:10.1007/s10916-008-9241-x · Cited by 127 · see citing works

Coronary heart disease diagnosis by artificial neural networks including genetic polymorphisms and clinical parameters

Atkov, O. Y., Gorokhova, S. G., Sboev, A. G., Generozov, E. V., Muraseeva, E. V., Морошкина, С. Ю., & Cherniy, N. N. (2012) · Journal of Cardiology · doi:10.1016/j.jjcc.2011.11.005 · Cited by 117 · see citing works

A Biomedical System Based on Artificial Neural Network and Principal Component Analysis for Diagnosis of the Heart Valve Diseases

Uğuz, H. (2010) · Journal of Medical Systems · doi:10.1007/s10916-010-9446-7 · Cited by 115 · see citing works

Introduction to neural computing

Jabri, M. A., Coggins, R. J., & Flower, B. (1996) · Springer eBooks · doi:10.1007/978-94-011-0525-5_2 · Cited by 102 · see citing works

Tuberculosis Disease Diagnosis Using Artificial Neural Network Trained with Genetic Algorithm

Elveren, E., & Yumuşak, N. (2009) · Journal of Medical Systems · doi:10.1007/s10916-009-9369-3 · Cited by 98 · see citing works

Computational Intelligence in Early Diabetes Diagnosis: A Review

Shankaracharya, N. A., Odedra, D., Samanta, S., & Vidyarthi, A. S. (2010) · The Review of Diabetic Studies · doi:10.1900/rds.2010.7.252 · Cited by 93 · see citing works

Non-invasive glucose monitoring

Arnold, M. A. (1996) · Current Opinion in Biotechnology · doi:10.1016/s0958-1669(96)80093-0 · Cited by 90 · see citing works

Artificial neural networks in pancreatic disease

Bartosch-Härlid, A., Andersson, B., Aho, U., Nilsson, J., & Andersson, R. E. (2008) · British journal of surgery · doi:10.1002/bjs.6239 · Cited by 78 · see citing works

Application of backpropagation neural networks to diagnosis of breast and ovarian cancer

Wilding, P. M., Morgan, M. A., Grygotis, A., Shoffner, M. A., & Rosato, E. F. (1994) · Cancer Letters · doi:10.1016/0304-3835(94)90097-3 · Cited by 77 · see citing works

Neural networks for optimization of high-performance capillary zone electrophoresis methods

Havel, J., Peña, E. M., Rojas-Hernándéz, A., Doucet, J. P., & Panaye, A. D. (1998) · Journal of Chromatography A · doi:10.1016/s0021-9673(97)00918-7 · Cited by 76 · see citing works

miRNA expressions in rectal cancer as predictors of response to neoadjuvant chemoradiation therapy

Kheirelseid, E. A. H., Miller, N., Chang, K. H., Curran, C. E., Hennessey, E., Sheehan, M., Newell, J., Lemetre, C., Balls, G., & Kerin, M. J. (2012) · International Journal of Colorectal Disease · doi:10.1007/s00384-012-1549-9 · Cited by 74 · see citing works

Disease-Free Survival after Hepatic Resection in Hepatocellular Carcinoma Patients: A Prediction Approach Using Artificial Neural Network

Ho, W., Lee, K., Chen, H.-Y., Ho, T., & Chiu, H. (2012) · PLoS ONE · doi:10.1371/journal.pone.0029179 · Cited by 74 · see citing works

Selecting critical clinical features for heart diseases diagnosis with a real-coded genetic algorithm

Yan, H., Zheng, J. J., Jiang, Y., Peng, C., & Xiao, S. (2007) · Applied Soft Computing · doi:10.1016/j.asoc.2007.05.017 · Cited by 66 · see citing works

The control of blood glucose in the critical diabetic patient

Dazzi, D., Taddei, F., Gavarini, A., Uggeri, E., Negro, R., & Pezzarossa, A. (2001) · Journal of Diabetes and its Complications · doi:10.1016/s1056-8727(00)00137-9 · Cited by 63 · see citing works

Diagnosis of hypoglycemic episodes using a neural network based rule discovery system

Chan, K. Y., Ling, S. H., Dillon, T. S., & Nguyen, H. T. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.02.020 · Cited by 63 · see citing works

Artificial neural networks for closed loop control of in silico and ad hoc type 1 diabetes

de Cañete, J. F., Gonzalez-Perez, S., & Ramos-Diaz, J. C. (2011) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2011.11.006 · Cited by 61 · see citing works

Artificial neural network in diagnosis of metastatic carcinoma in effusion cytology

Barwad, A. W., Dey, P., & Susheilia, S. (2011) · Cytometry Part B Clinical Cytometry · doi:10.1002/cyto.b.20632 · Cited by 58 · see citing works

A Study on Chronic Obstructive Pulmonary Disease Diagnosis Using Multilayer Neural Networks

Er, O., & Temurtaş, F. (2008) · Journal of Medical Systems · doi:10.1007/s10916-008-9148-6 · Cited by 55 · see citing works

Artificial neural networks combined with experimental design: A “soft” approach for chemical kinetics

Amato, F., González-Hernández, J. L., & Havel, J. (2012) · Talanta · doi:10.1016/j.talanta.2012.01.044 · Cited by 52 · see citing works

Automatic small bowel tumor diagnosis by using multi-scale wavelet-based analysis in wireless capsule endoscopy images

Barbosa, D. C., Roupar, D., Ramos, J., Tavares, A., & Lima, C. (2012) · BioMedical Engineering OnLine · doi:10.1186/1475-925x-11-3 · Cited by 52 · see citing works

Simulation studies on neural predictive control of glucose using the subcutaneous route

Trajanoski, Z., Regittnig, W., & Wach, P. (1998) · Computer Methods and Programs in Biomedicine · doi:10.1016/s0169-2607(98)00020-0 · Cited by 47 · see citing works

A fast and adaptive automated disease diagnosis method with an innovative neural network model

Alkım, E., Gürbüz, E., & Kılıç, E. (2012) · Neural Networks · doi:10.1016/j.neunet.2012.04.010 · Cited by 45 · see citing works

A New Approach to Detection of ECG Arrhythmias: Complex Discrete Wavelet Transform Based Complex Valued Artificial Neural Network

Özbay, Y. (2008) · Journal of Medical Systems · doi:10.1007/s10916-008-9205-1 · Cited by 45 · see citing works

Effective Diagnosis of Coronary Artery Disease Using The Rotation Forest Ensemble Method

Karabulut, E. M., & İbrikçі, T. (2011) · Journal of Medical Systems · doi:10.1007/s10916-011-9778-y · Cited by 44 · see citing works

Prediction of kinetics of doxorubicin release from sulfopropyl dextran ion-exchange microspheres using artificial neural networks

Li, Y., Rauth, A. M., & Wu, X. Y. (2005) · European Journal of Pharmaceutical Sciences · doi:10.1016/j.ejps.2004.12.005 · Cited by 44 · see citing works

Thrips (Thysanoptera) identification using artificial neural networks

Fedor, P., Malenovský, I., Vaňhara, J., Sierka, W., & Havel, J. (2008) · Bulletin of Entomological Research · doi:10.1017/s0007485308005750 · Cited by 44 · see citing works

Artificial Neural Networks Based Controller for Glucose Monitoring during Clamp Test

Catalogna, M., Cohen, E., Fishman, S., Halpern, Z., Nevo, U., & Ben‐Jacob, E. (2012) · PLoS ONE · doi:10.1371/journal.pone.0044587 · Cited by 41 · see citing works

A clinical decision support system using multilayer perceptron neural network to assess well being in diabetes.

Narasingarao, M. R., Manda, R., Sridhar, G. R., Madhu, K., & Rao, A. A. (2009) · PubMed · Cited by 39 · see citing works

Pretreatment prediction of the chemotherapeutic response of human glioma cell cultures using nuclear magnetic resonance spectroscopy and artificial neural networks.

El‐Deredy, W., Ashmore, S. M., Branston, N. M., Darling, J. L., Williams, S. R., & Thomas, D. G. T. (1997) · PubMed · Cited by 37 · see citing works

Neural networks for modelling of chemical reaction systems with complex kinetics: oxidation of 2-octanol with nitric acid

Molga, E. J., van Woezik, B. A. A., & Westerterp, K. R. (2000) · Chemical Engineering and Processing - Process Intensification · doi:10.1016/s0255-2701(99)00093-8 · Cited by 36 · see citing works

Inverse optimal neural control of blood glucose level for type 1 diabetes mellitus patients

Leon, B. S., Alanís, A. Y., Sanchez, E. N., Tellez, F. O., & Ruiz-Velazquez, E. (2012) · Journal of the Franklin Institute · doi:10.1016/j.jfranklin.2012.02.011 · Cited by 34 · see citing works

Feed Forward Artificial Neural Network: Tool for Early Detection of Ovarian Cancer

Thakur, A. (2011) · Scientia Pharmaceutica · doi:10.3797/scipharm.1105-11 · Cited by 32 · see citing works

Artificial Neural Networks for Classification in Metabolomic Studies of Whole Cells Using 1H Nuclear Magnetic Resonance

Brougham, D. F., Ivanova, G. I., Gottschalk, M. E., Collins, D. M., Eustace, A. J., O'Connor, R. A., & Havel, J. (2010) · BioMed Research International · doi:10.1155/2011/158094 · Cited by 31 · see citing works

Application of an artificial neural network in the prognosis of chronic myeloid leukemia.

Dey, P., Lamba, A., Kumari, S., & Marwaha, N. (2011) · PubMed · Cited by 31 · see citing works

Cluster Analysis and Artificial Neural Networks Multivariate Classification of Onion Varieties

Galdón, B. R., Peña‐Méndez, E. M., Havel, J., Rodríguez, E. M. R., & Romero, C. D. (2010) · Journal of Agricultural and Food Chemistry · doi:10.1021/jf102014j · Cited by 29 · see citing works

Artificial neural networks in gynaecological diseases: current and potential future applications.

Siristatidis, C. S., Chrelias, C. G., Pouliakis, A., Katsimanis, E., & Kassanos, D. (2010) · PubMed · Cited by 28 · see citing works

Larval Morphology and Anatomy of the Parasitoid Exorista larvarum (Diptera: Tachinidae), with an Emphasis on Cephalopharyngeal Skeleton and Digestive Tract

Michalková, V., Valigurová, A. B., Dindo, M. L., & Vaňhara, J. (2009) · Journal of Parasitology · doi:10.1645/ge-1673.1 · Cited by 28 · see citing works

Artificial neural network analysis to assess hypernasality in patients treated for oral or oropharyngeal cancer

de Bruijn, M., ten Bosch, L., Kuik, D. J., Langendijk, J. A., Leemans, C. R., & Leeuw, I. M. V. (2011) · Logopedics Phoniatrics Vocology · doi:10.3109/14015439.2011.606227 · Cited by 18 · see citing works

Automatic seizure detection based on star graph topological indices

Fernández-Blanco, E., Rivero, D., Rabuñal, J. R., Dorado, J., Pazos, A., & Munteanu, C. R. (2012) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2012.07.004 · Cited by 16 · see citing works

A clinical decision support system using multi-layer perceptron neural network to predict quality of life in diabetes

Rao, M. R. N., Sridhar, G. R., Madhu, K., & Rao, A. A. (2009) · Diabetes & Metabolic Syndrome Clinical Research & Reviews · doi:10.1016/j.dsx.2009.04.002 · Cited by 16 · see citing works

Finding biomarkers is getting easier

Bradley, B. P. (2012) · Ecotoxicology · doi:10.1007/s10646-011-0848-1 · Cited by 15 · see citing works

The use of artificial neural network in gastroenterology: the experience of the first 10 years

Pace, F., & Savarino, V. (2007) · European Journal of Gastroenterology & Hepatology · doi:10.1097/meg.0b013e3282f198e5 · Cited by 15 · see citing works

Standardizing clinical laboratory data for the development of transferable computer-based diagnostic programs.

Strike, P. W., Michaeloudis, A., & Green, A. (1986) · Clinical Chemistry · doi:10.1093/clinchem/32.1.22 · Cited by 9 · see citing works

Polyphasic approach applying artificial neural networks, molecular analysis and postabdomen morphology to West PalaearcticTachinaspp. (Diptera, Tachinidae)

Muráriková, N., Vaňhara, J., Tóthová, A., & Havel, J. (2010) · Bulletin of Entomological Research · doi:10.1017/s0007485310000295 · Cited by 8 · see citing works

All-optical spiking neurosynaptic networks with self-learning capabilities

Feldmann, J., Youngblood, N., Wright, C. D., Bhaskaran, H., & Pernice, W. H. P. (2019) · Nature · doi:10.1038/s41586-019-1157-8 · Cited by 1,527 · see citing works

On big data, artificial intelligence and smart cities

Allam, Z., & Dhunny, Z. A. (2019) · Cities · doi:10.1016/j.cities.2019.01.032 · Cited by 1,178 · see citing works

Application of Artificial Intelligence-Based Technologies in the Healthcare Industry: Opportunities and Challenges

Lee, D., & Yoon, S. N. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18010271 · Cited by 1,017 · see citing works

<scp>eD</scp> octor: machine learning and the future of medicine

Handelman, G. S., Kok, H. K., Chandra, R. V., Razavi, A. H., Lee, M. J., & Asadi, H. (2018) · Journal of Internal Medicine · doi:10.1111/joim.12822 · Cited by 956 · see citing works

Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks

Cui, H., Radosavljević, V., Chou, F., Lin, T.-H., Nguyen, T., Huang, T.-K., Schneider, J., & Djuric, N. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8793868 · Cited by 702 · see citing works

Artificial intelligence-enabled healthcare delivery

Reddy, S., Fox, J., & Purohit, M. P. (2019) · Journal of the Royal Society of Medicine · doi:10.1177/0141076818815510 · Cited by 635 · see citing works

An abstract domain for certifying neural networks

Singh, G., Gehr, T., Püschel, M., & Vechev, M. (2019) · Proceedings of the ACM on Programming Languages · doi:10.1145/3290354 · Cited by 625 · see citing works

The rise of intelligent matter

Kaspar, C., Ravoo, B. J., van der Wiel, W. G., Wegner, S. V., & Pernice, W. H. P. (2021) · Nature · doi:10.1038/s41586-021-03453-y · Cited by 526 · see citing works

Edge Machine Learning for AI-Enabled IoT Devices: A Review

Merenda, M., Porcaro, C., & Iero, D. (2020) · Sensors · doi:10.3390/s20092533 · Cited by 523 · see citing works

Machine Learning Methods for Small Data Challenges in Molecular Science

Dou, B., Zhu, Z., Merkurjev, E., Ke, L., Chen, L., Jian, J., Zhu, Y., Liu, J., Zhang, B., & Wei, G. (2023) · Chemical Reviews · doi:10.1021/acs.chemrev.3c00189 · Cited by 521 · see citing works

Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges

Banaee, H., Ahmed, M., & Loutfi, A. (2013) · Sensors · doi:10.3390/s131217472 · Cited by 452 · see citing works

Machine learning techniques for breast cancer computer aided diagnosis using different image modalities: A systematic review

Yassin, N. I. R., Omran, S., Houby, E. M. F. E., & Allam, H. (2017) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2017.12.012 · Cited by 389 · see citing works

Artificial Intelligence Versus Clinicians in Disease Diagnosis: Systematic Review

Shen, J., Zhang, C. J. P., Jiang, B., Chen, J., Song, J., Liu, Z., He, Z., Wong, S. Y., Fang, P.-H., & Ming, W.-K. (2019) · JMIR Medical Informatics · doi:10.2196/10010 · Cited by 373 · see citing works

A Decade Survey of Content Based Image Retrieval Using Deep Learning

Dubey, S. R. (2021) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2021.3080920 · Cited by 365 · see citing works

Deep learning approach for diabetes prediction using PIMA Indian dataset

Naz, H. U., & Ahuja, S. (2020) · Journal of Diabetes & Metabolic Disorders · doi:10.1007/s40200-020-00520-5 · Cited by 359 · see citing works

Ethical dimensions of using artificial intelligence in health care

(2019) · AMA Journal of Ethics · doi:10.1001/amajethics.2019.121 · Cited by 328 · see citing works

Predicting factors for survival of breast cancer patients using machine learning techniques

Ganggayah, M. D., Taib, N. A. M., Har, Y. C., Lió, P., & Dhillon, S. K. (2019) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-019-0801-4 · Cited by 313 · see citing works

The role of artificial neural network and machine learning in utilizing spatial information

Goel, A., Kumar, A., & Kumar, A. (2022) · Spatial Information Research · doi:10.1007/s41324-022-00494-x · Cited by 304 · see citing works

Breath Analysis as a Potential and Non-Invasive Frontier in Disease Diagnosis: An Overview

Pereira, J. A. M., Porto-Figueira, P., Cavaco, C., Taunk, K., Rapole, S., Dhakne, R., Nagarajaram, H. A., & Câmara, J. S. (2015) · Metabolites · doi:10.3390/metabo5010003 · Cited by 296 · see citing works

Handling limited datasets with neural networks in medical applications: A small-data approach

Shaikhina, T., & Khovanova, N. A. (2017) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2016.12.003 · Cited by 292 · see citing works

Current breathomics—a review on data pre-processing techniques and machine learning in metabolomics breath analysis

Smolinska, A., Hauschild, A.-C., Fijten, R., Dallinga, J. W., Baumbach, J., & van Schooten, F. (2014) · Journal of Breath Research · doi:10.1088/1752-7155/8/2/027105 · Cited by 220 · see citing works

Performance analysis of various machine learning-based approaches for detection and classification of lung cancer in humans

Singh, G. A. P., & Gupta, P. K. (2018) · Neural Computing and Applications · doi:10.1007/s00521-018-3518-x · Cited by 215 · see citing works

Recent Advancements in Fruit Detection and Classification Using Deep Learning Techniques

Ukwuoma, C. C., Zhi-guang, Q., Heyat, M. B. B., Ali, L., Almaspoor, Z., & Monday, H. N. (2022) · Mathematical Problems in Engineering · doi:10.1155/2022/9210947 · Cited by 192 · see citing works

The past, present and future role of artificial intelligence in imaging

Fazal, M. I., Patel, M. S. E., Tye, J., & Gupta, Y. (2018) · European Journal of Radiology · doi:10.1016/j.ejrad.2018.06.020 · Cited by 178 · see citing works

Computer vision and machine learning for medical image analysis: recent advances, challenges, and way forward

Elyan, E., Vuttipittayamongkol, P., Johnston, P., Martin, K., McPherson, K., Moreno‐García, C. F., Jayne, C., & Sarker, M. M. K. (2022) · Artificial Intelligence Surgery · doi:10.20517/ais.2021.15 · Cited by 172 · see citing works

Data preprocessing for heart disease classification: A systematic literature review

Benhar, H., Idri, A., & Fernández‐Alemán, J. L. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105635 · Cited by 162 · see citing works

Ethics of Artificial Intelligence in Medicine and Ophthalmology

Abdullah, Y. I., Schuman, J. S., Shabsigh, R., Caplan, A. L., & Al‐Aswad, L. A. (2021) · Asia-Pacific Journal of Ophthalmology · doi:10.1097/apo.0000000000000397 · Cited by 159 · see citing works

An investigation of XGBoost-based algorithm for breast cancer classification

Liew, X. Y., Hameed, N., & Clos, J. (2021) · Machine Learning with Applications · doi:10.1016/j.mlwa.2021.100154 · Cited by 159 · see citing works

Machine Learning in Amyotrophic Lateral Sclerosis: Achievements, Pitfalls, and Future Directions

Grollemund, V., Pradat, P., Querin, G., Delbot, F., Chat, G. L., Pradat‐Peyre, J., & Bede, P. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00135 · Cited by 154 · see citing works

Machine learning for characterizing risk of type 2 diabetes mellitus in a rural Chinese population: the Henan Rural Cohort Study

Zhang, L., Wang, Y., Niu, M., Wang, C., & Wang, Z. (2020) · Scientific Reports · doi:10.1038/s41598-020-61123-x · Cited by 153 · see citing works

XAI Framework for Cardiovascular Disease Prediction Using Classification Techniques

Guleria, P., Srinivasu, P. N., Ahmed, S., Almusallam, N., & Alarfaj, F. K. (2022) · Electronics · doi:10.3390/electronics11244086 · Cited by 153 · see citing works

A DE-ANN Inspired Skin Cancer Detection Approach Using Fuzzy C-Means Clustering

Kumar, M., Alshehri, M., AlGhamdi, R. A., Sharma, P., & Deep, V. (2020) · Mobile Networks and Applications · doi:10.1007/s11036-020-01550-2 · Cited by 152 · see citing works

Neural Network-Based Coronary Heart Disease Risk Prediction Using Feature Correlation Analysis

Kim, J. K., & Kang, S. (2017) · Journal of Healthcare Engineering · doi:10.1155/2017/2780501 · Cited by 148 · see citing works

A Comparative Study of Neural Networks and ANFIS for Forecasting Attendance Rate of Soccer Games

Şahin, M., & Erol, R. (2017) · Mathematical and Computational Applications · doi:10.3390/mca22040043 · Cited by 144 · see citing works

Addressing Binary Classification over Class Imbalanced Clinical Datasets Using Computationally Intelligent Techniques

Kumar, V., Lalotra, G. S., Sasikala, P., Rajput, D. S., Kaluri, R., Lakshmanna, K., Shorfuzzaman, M., Alsufyani, A., & Uddin, M. (2022) · Healthcare · doi:10.3390/healthcare10071293 · Cited by 142 · see citing works

Acquaintance to Artificial Neural Networks and use of artificial intelligence as a diagnostic tool for tuberculosis: A review

Dande, P. R., & Samant, P. (2017) · Tuberculosis · doi:10.1016/j.tube.2017.09.006 · Cited by 140 · see citing works

Emotion recognition based on EEG features in movie clips with channel selection

Özerdem, M. S., & Polat, H. (2017) · Brain Informatics · doi:10.1007/s40708-017-0069-3 · Cited by 136 · see citing works

Hybrid stacked ensemble combined with genetic algorithms for diabetes prediction

Abdollahi, J., & Nouri-Moghaddam, B. (2022) · Iran Journal of Computer Science · doi:10.1007/s42044-022-00100-1 · Cited by 133 · see citing works

Towards Imperceptible and Robust Adversarial Example Attacks Against Neural Networks

Luo, B., Liu, Y., Wei, L., & Xu, Q. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11499 · Cited by 130 · see citing works

Robust Machine Learning Systems: Challenges,Current Trends, Perspectives, and the Road Ahead

Shafique, M., Naseer, M., Theocharides, T., Kyrkou, C., Mutlu, O., Orosa, L., & Choi, J. (2020) · IEEE Design and Test · doi:10.1109/mdat.2020.2971217 · Cited by 128 · see citing works

Where medical education meets artificial intelligence: ‘Does technology care?’

van der Niet, A. G., & Bleakley, A. (2020) · Medical Education · doi:10.1111/medu.14131 · Cited by 128 · see citing works

IOT AND AI IN HEALTHCARE: A SYSTEMATIC LITERATURE REVIEW

(2018) · Issues in Information Systems · doi:10.48009/3_iis_2018_33-41 · Cited by 125 · see citing works

Medical data classification using interval type-2 fuzzy logic system and wavelets

Nguyen, T. T., Khosravi, A., Creighton, D., & Nahavandi, S. (2015) · Applied Soft Computing · doi:10.1016/j.asoc.2015.02.016 · Cited by 121 · see citing works

Early detection of the breast cancer using infrared technology – A comprehensive review

Mashekova, A., Zhao, Y., Ng, E. Y. K., Zarikas, V., Fok, S. C., & Mukhmetov, O. (2021) · Thermal Science and Engineering Progress · doi:10.1016/j.tsep.2021.101142 · Cited by 121 · see citing works

A review of stock market prediction with Artificial neural network (ANN)

Chang, S. V., Soon, G. K., On, C. K., Alfred, R., & Anthony, P. (2013) · IEEE International Conference on Control System, Computing and Engineering · doi:10.1109/iccsce.2013.6720012 · Cited by 116 · see citing works

Machine Learning for Predictive Modelling based on Small Data in Biomedical Engineering

Shaikhina, T., Lowe, D., Daga, S., Briggs, D., Higgins, R. M., & Khovanova, N. A. (2015) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2015.10.185 · Cited by 115 · see citing works

MedMetaverse: Medical Care of Chronic Disease Patients and Managing Data Using Artificial Intelligence, Blockchain, and Wearable Devices State-of-the-Art Methodology

Murala, D. K., Panda, S. K., & Dash, S. P. (2023) · IEEE Access · doi:10.1109/access.2023.3340791 · Cited by 114 · see citing works

Three Problems with Big Data and Artificial Intelligence in Medicine

Chin-Yee, B. H., & Upshur, R. E. G. (2019) · Perspectives in biology and medicine · doi:10.1353/pbm.2019.0012 · Cited by 113 · see citing works

Use of predictive algorithms in-home monitoring of chronic obstructive pulmonary disease and asthma: A systematic review

Sanchez-Morillo, D., Fernandez-Granero, M. A., & Leon-Jimenez, A. (2016) · Chronic Respiratory Disease · doi:10.1177/1479972316642365 · Cited by 110 · see citing works

An IoT-based real-time intelligent monitoring and notification system of cold storage

Afreen, H., & Bajwa, I. S. (2021) · IEEE Access · doi:10.1109/access.2021.3056672 · Cited by 109 · see citing works

Machine Learning and Big Data Provide Crucial Insight for Future Biomaterials Discovery and Research

Kerner, J., Dogan, A. B., & von Recum, H. A. (2020) · Acta Biomaterialia · doi:10.1016/j.actbio.2021.05.053 · Cited by 106 · see citing works

Neural networks for computer-aided diagnosis in medicine: A review

lin, D., Vasilakos, A. V., Tang, Y., & Yao, Y. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.08.039 · Cited by 105 · see citing works

DNA methylation in ELOVL2 and C1orf132 correctly predicted chronological age of individuals from three disease groups

Spólnicka, M., Pośpiech, E., Pepłońska, B., Zbieć-Piekarska, R., Makowska, Ż., Pięta, A., Karłowska-Pik, J., Ziemkiewicz, B., Wężyk, M., Gasperowicz, P., et al. (2018) · International Journal of Legal Medicine · doi:10.1007/s00414-017-1636-0 · Cited by 105 · see citing works

Nonlinear germanium-silicon photodiode for activation and monitoring in photonic neuromorphic networks

Shi, Y., Ren, J., Chen, G., Liu, W., Jin, C., Guo, X., Yu, Y., & Zhang, X. (2022) · Nature Communications · doi:10.1038/s41467-022-33877-7 · Cited by 105 · see citing works

Implementation of a Web Application to Predict Diabetes Disease: An Approach Using Machine Learning Algorithm

Dey, S. K., Hossain, A., & Rahman, M. M. (2018) · International Conference on Computer and Information Technology (ICCIT) · doi:10.1109/iccitechn.2018.8631968 · Cited by 103 · see citing works

Accelerate global sensitivity analysis using artificial neural network algorithm: Case studies for combustion kinetic model

Li, S., Yang, B., & Qi, F. (2016) · Combustion and Flame · doi:10.1016/j.combustflame.2016.03.028 · Cited by 100 · see citing works

Overview of artificial neural network models in the biomedical domain

Renganathan, V. (2019) · Bratislavské lekárske listy/Bratislava medical journal · doi:10.4149/bll_2019_087 · Cited by 100 · see citing works

Deep Machine Learning for Oral Cancer: From Precise Diagnosis to Precision Medicine

Alabi, R. O., Almangush, A., Elmusrati, M. S., & Mäkitie, A. A. (2022) · Frontiers in Oral Health · doi:10.3389/froh.2021.794248 · Cited by 100 · see citing works

Predicting coronary artery disease: a comparison between two data mining algorithms

Ayatollahi, H., Gholamhosseini, L., & ‎Salehi, M. (2019) · BMC Public Health · doi:10.1186/s12889-019-6721-5 · Cited by 98 · see citing works

PSO-ANN based diagnostic model for the early detection of dengue disease

Gambhir, S., Malik, S. K., & Kumar, Y. (2017) · New Horizons in Translational Medicine · doi:10.1016/j.nhtm.2017.10.001 · Cited by 97 · see citing works

Diagnosis of urinary tract infection based on artificial intelligence methods

Özkan, İ. A., Köklü, M., & Sert, İ. Ü. (2018) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.10.007 · Cited by 96 · see citing works

Deep Learning: Current and Emerging Applications in Medicine and Technology

Akay, A. R., & Hess, H. (2019) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2019.2894713 · Cited by 96 · see citing works

Colorectal Disease Classification Using Efficiently Scaled Dilation in Convolutional Neural Network

Poudel, S., Kim, Y. J., Vo, D. M., & Lee, S.-W. (2020) · IEEE Access · doi:10.1109/access.2020.2996770 · Cited by 95 · see citing works

Fully Complex-Valued Dendritic Neuron Model

Gao, S., Zhou, M., Wang, Z., Sugiyama, D., Cheng, J., Wang, J., & Todo, Y. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3105901 · Cited by 95 · see citing works

Explainable Artificial Intelligence (XAI) in healthcare: interpretable models for clinical decision support

Rane, N. L., Choudhary, S., & Rane, J. (2024) · doi:10.61577/scpnmia.2024.100006 · Cited by 92 · see citing works

Using machine learning to predict hypertension from a clinical dataset

Lafrenière, D., Zulkernine, F. H., Barber, D., & Martin, K. (2016) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci.2016.7849886 · Cited by 90 · see citing works

Advances in automated tongue diagnosis techniques

Tania, M. H., Lwin, K. T., & Hossain, M. A. (2018) · Integrative Medicine Research · doi:10.1016/j.imr.2018.03.001 · Cited by 90 · see citing works

A Study of Artificial Neural Network Training Algorithms for Classification of Cardiotocography Signals

Cömert, Z., & Kocamaz, A. F. (2017) · Bitlis Eren University Journal of Science and Technology · doi:10.17678/beuscitech.338085 · Cited by 89 · see citing works

A “Third Wheel” Effect in Health Decision Making Involving Artificial Entities: A Psychological Perspective

Triberti, S., Durosini, I., & Pravettoni, G. (2020) · Frontiers in Public Health · doi:10.3389/fpubh.2020.00117 · Cited by 89 · see citing works

A hybrid cost-sensitive ensemble for heart disease prediction

Qi, Z., & Zhang, Z. (2021) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-021-01436-7 · Cited by 89 · see citing works

Biomedical Ethical Aspects Towards the Implementation of Artificial Intelligence in Medical Education

Busch, F., Adams, L. C., & Bressem, K. K. (2023) · Medical Science Educator · doi:10.1007/s40670-023-01815-x · Cited by 88 · see citing works

Artificial Neural Networks as a powerful numerical tool to classify specific features of a tooth based on 3D scan data

Raith, S., Vogel, E. P., Anees, N., Keul, C., Güth, J., Edelhoff, D., & Fischer, H. (2016) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2016.11.013 · Cited by 87 · see citing works

Artificial Intelligence in Personalized Medicine Application of AI Algorithms in Solving Personalized Medicine Problems

Awwalu, J., Garba, A. G., Ghazvini, A., & Atuah, R. (2014) · International Journal of Computer Theory and Engineering · doi:10.7763/ijcte.2015.v7.999 · Cited by 86 · see citing works

Conceptual Structure and Current Trends in Artificial Intelligence, Machine Learning, and Deep Learning Research in Sports: A Bibliometric Review

Dindorf, C., Bartaguiz, E., Gassmann, F., & Fröhlich, M. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph20010173 · Cited by 86 · see citing works

Back-propagation neural network-based reconstruction algorithm for diffuse optical tomography

Feng, J., Sun, Q., Li, Z., Sun, Z., & Jia, K. (2018) · Journal of Biomedical Optics · doi:10.1117/1.jbo.24.5.051407 · Cited by 84 · see citing works

A Comparison of Deep Learning Techniques for Arterial Blood Pressure Prediction

Paviglianiti, A., Randazzo, V., Villata, S., Cirrincione, G., & Pasero, E. G. A. (2021) · Cognitive Computation · doi:10.1007/s12559-021-09910-0 · Cited by 84 · see citing works

Responsible Use of Artificial Intelligence in Dentistry: Survey on Dentists’ and Final-Year Undergraduates’ Perspectives

Roganović, J., Radenković, M., & Miličić, B. (2023) · Healthcare · doi:10.3390/healthcare11101480 · Cited by 84 · see citing works

Optimizing the Learning Process of Feedforward Neural Networks Using Lightning Search Algorithm

Faris, H., Aljarah, I., Al–Madi, N., & Mirjalili, S. (2016) · International Journal of Artificial Intelligence Tools · doi:10.1142/s0218213016500330 · Cited by 81 · see citing works

Improving prognosis and reducing decision regret for pancreatic cancer treatment using artificial neural networks

Walczak, S. M., & Velanovich, V. (2017) · Decision Support Systems · doi:10.1016/j.dss.2017.12.007 · Cited by 81 · see citing works

Machine-learning algorithms define pathogen-specific local immune fingerprints in peritoneal dialysis patients with bacterial infections

Zhang, J., Friberg, I. M., Kift-Morgan, A. P., Parekh, G., Morgan, M. P. G., Liuzzi, A. R., Lin, C., Donovan, K. L., Colmont, C. S., Morgan, P., et al. (2017) · Kidney International · doi:10.1016/j.kint.2017.01.017 · Cited by 80 · see citing works

Conceptualizations of Big Data and their epistemological claims in healthcare: A discourse analysis

Stevens, M., Wehrens, R., & Bont, A. A. D. (2018) · Big Data & Society · doi:10.1177/2053951718816727 · Cited by 78 · see citing works

Fast learning of fiber orientation distribution function for MR tractography using convolutional neural network

Lin, Z., Gong, T., Wang, K., Li, Z., He, H., Tong, Q., Feng, Y., & Zhong, J. (2019) · Medical Physics · doi:10.1002/mp.13555 · Cited by 78 · see citing works

Artificial Intelligence (AI) Assisted History Matching

Shahkarami, A., Mohaghegh, S. D., Gholami, V., & Haghighat, S. A. (2014) · SPE Western North American and Rocky Mountain Joint Meeting · doi:10.2118/169507-ms · Cited by 78 · see citing works

Machine Learning Analysis of Digital Clock Drawing Test Performance for Differential Classification of Mild Cognitive Impairment Subtypes Versus Alzheimer’s Disease

Binaco, R., Calzaretto, N., Epifano, J. R., McGuire, S., Umer, M., Emrani, S., Wasserman, V., Libon, D. J., & Robi Polikar, A. (2020) · Journal of the International Neuropsychological Society · doi:10.1017/s1355617720000144 · Cited by 77 · see citing works

Artificial neural network detects human uncertainty

Hramov, A. E., Frolov, N. S., Maksimenko, V., Makarov, V. V., Koronovskii, A. A., García-Prieto, J., Antón‐Toro, L. F., Maestú, F., & Pisarchik, A. N. (2018) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.5002892 · Cited by 76 · see citing works

Deep Learning for Improved Risk Prediction in Surgical Outcomes

Jalali, A., Lonsdale, H., Do, N., Peck, J., Gupta‐Malhotra, M., Kutty, S., Ghazarian, S. R., Jacobs, J. P., Rehman, M., & Ahumada, L. M. (2020) · Scientific Reports · doi:10.1038/s41598-020-62971-3 · Cited by 76 · see citing works

A survey on applying machine learning techniques for management of diseases

Houby, E. M. F. E. (2018) · Journal of Applied Biomedicine · doi:10.1016/j.jab.2018.01.002 · Cited by 76 · see citing works

High-reward, high-risk technologies? An ethical and legal account of AI development in healthcare

Corfmat, M., Martineau, J. T., & Régis, C. (2025) · BMC Medical Ethics · doi:10.1186/s12910-024-01158-1 · Cited by 75 · see citing works

A hybrid quantum–classical neural network with deep residual learning

Liang, Y., Peng, W., Zheng, Z., Sílven, O., & Zhao, G. (2021) · Neural Networks · doi:10.1016/j.neunet.2021.05.028 · Cited by 74 · see citing works

The Role of Humans in Surgery Automation

Fosch‐Villaronga, E., Khanna, P., Drukarch, H., & Custers, B. H. M. (2022) · International Journal of Social Robotics · doi:10.1007/s12369-022-00875-0 · Cited by 74 · see citing works

Application of the convolutional neural networks and supervised deep-learning methods for osteosarcoma bone cancer detection

Gawade, S. D., Bhansali, A., Patil, K., & Shaikh, D. (2023) · Healthcare Analytics · doi:10.1016/j.health.2023.100153 · Cited by 74 · see citing works

A stacking classifiers model for detecting heart irregularities and predicting Cardiovascular Disease

Mohapatra, S., Maneesha, S., Mohanty, S., Patra, P. K., Bhoi, S. K., Sahoo, K. S., & Gandomi, A. H. (2022) · Healthcare Analytics · doi:10.1016/j.health.2022.100133 · Cited by 72 · see citing works

Medical Diagnosis Using Machine Learning: A Statistical Review

Bhavsar, K. A., Singla, J., Al‐Otaibi, Y. D., Song, O.-Y., Zikria, Y. B., & Bashir, A. K. (2021) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2021.014604 · Cited by 71 · see citing works

Neural Network Prediction of ICU Length of Stay Following Cardiac Surgery Based on Pre-Incision Variables

Lafaro, R. J., Pothula, S., Kubal, K. P., Inchiosa, M. E., Pothula, V., Yuan, S. C., Maerz, D. A., Montes, L., Oleszkiewicz, S. M., Yusupov, A., et al. (2015) · PLoS ONE · doi:10.1371/journal.pone.0145395 · Cited by 70 · see citing works

Development of a Laparoscopic Box Trainer Based on Open Source Hardware and Artificial Intelligence for Objective Assessment of Surgical Psychomotor Skills

Alonso-Silverio, G. A., Escamirosa, F. P., Bruno-Sanchez, R., Ortiz-Simón, J. L., Muñoz, R., Minor‐Martínez, A., & Alarcón-Paredes, A. (2018) · Surgical Innovation · doi:10.1177/1553350618777045 · Cited by 70 · see citing works

In-depth comparison of deep artificial neural network architectures on seismic events classification

Canário, J. P., de Mello, R. F., Curilem, M., Huenupán, F., & Rios, R. A. (2020) · Journal of Volcanology and Geothermal Research · doi:10.1016/j.jvolgeores.2020.106881 · Cited by 69 · see citing works

Bottom hole pressure estimation using hybridization neural networks and grey wolves optimization

Amar, M. N., Zeraibi, N., & Redouane, K. (2018) · Petroleum · doi:10.1016/j.petlm.2018.03.013 · Cited by 69 · see citing works

Pathological neural networks and artificial neural networks in ALS: diagnostic classification based on pathognomonic neuroimaging features

Bede, P., Murad, A. A., & Hardiman, O. M. (2021) · Journal of Neurology · doi:10.1007/s00415-021-10801-5 · Cited by 68 · see citing works

Chalcogenide phase-change devices for neuromorphic photonic computing

Brückerhoff‐Plückelmann, F., Feldmann, J., Wright, C. D., Bhaskaran, H., & Pernice, W. H. P. (2021) · Journal of Applied Physics · doi:10.1063/5.0042549 · Cited by 65 · see citing works

Application of Multi Layer Neural Network in Medical Diagnosis: an Efficient Survey

Rana, A., Rawat, A. S., Bahuguna, H., & Bijalwan, A. (2018) · International Journal of Engineering & Technology · doi:10.14419/ijet.v7i3.34.19366 · Cited by 1 · see citing works

Artificial Neural Network in Medicine

Albu, A., & Ungureanu, L. (2008)

ปญญาประดษฐทางการแพทย

Koyvanich, K. (2021) · VCHPK Health and Public Health Sciences Journal

Medical diagnosis using artificial neural networks

Begum, A., Rahman, M. M., & Jahan, S. (2024) · Mathematics in Applied Sciences and Engineering · doi:10.5206/mase/17138 · Cited by 1 · see citing works

Artificial neural networks for medical diagnosis using biomedical dataset

Shayea, Q. A., Refae, G. A. E., & Yaseen, S. G. (2013) · International Journal of Behavioural and Healthcare Research · doi:10.1504/ijbhr.2013.054519 · Cited by 22 · see citing works