Artificial neural networks in medical diagnosis

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Artificial Neural Networks

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Artificial Intelligence in Medical Diagnosis

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

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Artificial neural networks for diagnosis and survival prediction in colon cancer

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Segmentation of multiple sclerosis lesions in MR images: a review

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Role of artificial intelligence and vibrational spectroscopy in cancer diagnostics

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Artificial neural networks approach to early lung cancer detection

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Machine Learning Algorithms

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Virtual surface morphology generation of Ti-6Al-4V directed energy deposition via conditional generative adversarial network

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General implementation of quantum physics-informed neural networks

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Artificial Neural Networks and risk stratification models in Emergency Departments: The policy maker's perspective

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Artificial neural networks as classification and diagnostic tools for lymph node-negative breast cancers

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Application of texture analysis method for mammogram density classification

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Comparative Performance of Deep Learning and Machine Learning Algorithms on Imbalanced Handwritten Data

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Machine learning-based diagnosis for disseminated intravascular coagulation (DIC): Development, external validation, and comparison to scoring systems

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Deep learning time series prediction models in surveillance data of hepatitis incidence in China

Xia, Z., Qin, L., Ning, Z., & Zhang, X. (2022) · PLoS ONE · doi:10.1371/journal.pone.0265660 · Cited by 18

Artificial Intelligence for Healthcare and Social Services: Optimizing Resources and Promoting Sustainability

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Applying artificial neural networks to predict communication risks in the emergency department

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Artificial Neural Network and Mobile Applications in Medical Diagnosis

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Prediction of cervical spine injury in young pediatric patients: an optimal trees artificial intelligence approach

Bertsimas, D., Masiakos, P. T., Mylonas, K. S., & Wiberg, H. (2019) · Journal of Pediatric Surgery · doi:10.1016/j.jpedsurg.2019.03.007 · Cited by 18

A fitting machine learning prediction model for short-term mortality following percutaneous catheterization intervention: a nationwide population-based study

Hsieh, M.-H., Lin, S., Lin, C., Hsieh, M.-J., Hsu, W., Ju, S.-W., Lin, C., Hsu, C. Y., & Kao, C. (2019) · Annals of Translational Medicine · doi:10.21037/atm.2019.12.21 · Cited by 18

On Healthcare Robots: Concepts, definitions, and considerations for healthcare robot governancePreprint

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Artificial Intelligence and Machine Learning‐Based Manufacturing and Drug Product Marketing

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The Application of an Artificial Neural Network to Support Decision Making in Edentulous Maxillary Implant Prostheses

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Use of Orange Data Mining Toolbox for Data Analysis in Clinical Decision Making

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Robust Estimation of the Chronological Age of Children and Adolescents Using Tooth Geometry Indicators and POD-GP

Zaborowicz, K., Garbowski, T., Biedziak, B., & Zaborowicz, M. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19052952 · Cited by 16

Machine Learning for Demand Forecasting in Manufacturing

Sistla, S. M. K., Krishnamoorthy, G., Jeyaraman, J., & Konidena, B. K. (2024) · International Journal For Multidisciplinary Research · doi:10.36948/ijfmr.2024.v06i01.14204 · Cited by 16

Policy Modeling and Applications: State‐of‐the‐Art and Perspectives

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A novel model for predicting fatty liver disease by means of an artificial neural network

Chen, Y.-S., Chen, D., Shen, C., Chen, M., Jin, C., Xu, C., Yu, C., & Li, Y. (2020) · Gastroenterology report · doi:10.1093/gastro/goaa035 · Cited by 16

Artificial neural networks as clinical decision support systems

Shafi, I., Ansari, S., Din, S., Jeon, G., & Paul, A. (2021) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.6342 · Cited by 19

Explainable fuzzy neural network with easy-to-obtain physiological features for screening obstructive sleep apnea-hypopnea syndrome

Juang, C., Wen, C.-Y., Chang, K.-M., Chen, Y., Wu, M., & Huang, W. (2021) · Sleep Medicine · doi:10.1016/j.sleep.2021.07.012 · Cited by 16

Artificial neural network-boosted Cardiac Arrest Survival Post-Resuscitation In-hospital (CASPRI) score accurately predicts outcome in cardiac arrest patients treated with targeted temperature management

Chou, S., Bamodu, O. A., Chiu, W.-T., Hong, C., Chan, L., & Chung, C. (2022) · Scientific Reports · doi:10.1038/s41598-022-11201-z · Cited by 16

A Decision Support System for Tuberculosis Diagnosability

Walia, N., Singh, H., Tiwari, S. K., & Sharma, A. (2015) · International Journal on Soft Computing · doi:10.5121/ijsc.2015.6301 · Cited by 16

Segregation of Ripe and Raw Bananas Using Convolutional Neural Network

Upadhyay, A., Singh, S. R. K., & Kanojiya, S. (2023) · Procedia Computer Science · doi:10.1016/j.procs.2023.01.028 · Cited by 16

Multivariate Calibration Approach for Quantitative Determination of Cell-Line Cross Contamination by Intact Cell Mass Spectrometry and Artificial Neural Networks

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Early detection of colorectal adenocarcinoma: a clinical decision support tool based on plasma porphyrin accumulation and risk factors

Lualdi, M., Cavalleri, A., Battaglia, L., Colombo, A., Garrone, G., Morelli, D., Pignoli, E., Sottotetti, E., & Leo, E. (2018) · BMC Cancer · doi:10.1186/s12885-018-4754-2 · Cited by 16

Skin cancer diagnosed using artificial intelligence on clinical images

van der Waal, I. (2017) · Oral Diseases · doi:10.1111/odi.12668 · Cited by 16

Artificial neural network tools for computerised data modeling and processing

Křenek, J., Kuča, K., Krejcar, O., Marešová, P., Sobĕslav, V., & Blažek, P. (2014) · IEEE International Symposium on Computational Intelligence and Informatics (CINTI) · doi:10.1109/cinti.2014.7028685 · Cited by 15

From Fuzzy Expert System to Artificial Neural Network: Application to Assisted Speech Therapy

Schipor, O.-A., Geman, O., Chiuchisan, I., & Covașă, M. (2016) · InTech eBooks · doi:10.5772/63332 · Cited by 15

Exploring bias risks in artificial intelligence and targeted medicines manufacturing

Nwebonyi, N., & McKay, F. (2024) · BMC Medical Ethics · doi:10.1186/s12910-024-01112-1 · Cited by 15

Removal of Congo red by magnetic nano-alumina using response surface methodology and artificial neural network

Hassani, S., Shirani, M., Semnani, A., Hassani, M., & Firooz, A. (2017) · Desalination and Water Treatment · doi:10.5004/dwt.2017.0018 · Cited by 15

Data assimilation and multisource decision-making in systems biology based on unobtrusive Internet-of-Things devices

Tang, W.-H., Ho, W., & Chen, Y. J. (2018) · BioMedical Engineering OnLine · doi:10.1186/s12938-018-0574-5 · Cited by 15

Prophylactic antibiotic bundle compliance and surgical site infections: an artificial neural network analysis

Walczak, S. M., Davila, M., & Velanovich, V. (2019) · Patient Safety in Surgery · doi:10.1186/s13037-019-0222-4 · Cited by 15

Comparison of the Particle Swarm Optimization with the Genetic Algorithms as a Training for Multilayer Perceptron Technique to Diagnose Thyroid Functional Disease

Sharifi, A., & Alizadeh, K. (2020) · Shiraz E-Medical Journal · doi:10.5812/semj.100351 · Cited by 15

Artificial Neural Networks for Medical Diagnosis: A Review of Recent Trends

Fraser, E. A., Okonkwo, & Obikwelu, R. (2020) · International Journal of Computer Science & Engineering Survey · doi:10.5121/ijcses.2020.11301 · Cited by 15

Genetic Algorithm to Optimize k-Nearest Neighbor Parameter for Benchmarked Medical Datasets Classification

Prasetio, R. T. (2020) · Jurnal Online Informatika · doi:10.15575/join.v5i2.656 · Cited by 15

Domain wall dynamics in (Co/Ni)n nanowire with anisotropy energy gradient for neuromorphic computing applications

Mah, W. L. W., Kumar, D., Jin, T., & Piramanayagam, S. N. (2021) · Journal of Magnetism and Magnetic Materials · doi:10.1016/j.jmmm.2021.168131 · Cited by 15

A novel method for the discrimination of Hawthorn and its processed products using an intelligent sensory system and artificial neural networks

Xie, D., Peng, W., Chen, J.-C., Li, L., Zhao, C., Yang, S.-L., Xu, M. H., Wu, C., & Ai, L. (2016) · Food Science and Biotechnology · doi:10.1007/s10068-016-0239-8 · Cited by 15

A Forecast of Coronary Heart Disease using Proficient Machine Learning Algorithms

Gaba, S., Aggarwal, A., Nagpal, S., Kumar, D., & Singh, P. (2021) · International Conference on Image Information Processing (ICIIP) · doi:10.1109/iciip53038.2021.9702640 · Cited by 14

Verifying Global Two-Safety Properties in Neural Networks with Confidence

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Construction of medical equipment-based doctor health monitoring system

Wang, S., Cheng, S., Zhou, X., Mao, Y., Li, Y., Long, G., Li, C., Liu, W., & Long, X. (2019) · Journal of Medical Systems · doi:10.1007/s10916-019-1255-z · Cited by 14

Hospital Readmission Prediction using Machine Learning Techniques

Alajmani, S., & Elazhary, H. (2019) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2019.0100425 · Cited by 14

A Novel Classification Method using Effective Neural Network and Quantitative Magnetization Transfer Imaging of Brain White Matter in Relapsing Remitting Multiple Sclerosis

Fooladi, M., Sharini, H., Masjoodi, S., & Khodamoradi, E. (2018) · Journal of Biomedical Physics and Engineering · doi:10.31661/jbpe.v8i4dec.926 · Cited by 14

Intact Cell Mass Spectrometry as a Quality Control Tool for Revealing Minute Phenotypic Changes of Cultured Human Embryonic Stem Cells

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Proof transfer for fast certification of multiple approximate neural networks

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Simulation of Acoustic Properties of Plaster Matrix Composite MATERIAL Reinforced with Corn Stem Fibers

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Decision Support System for Diabetes Mellitus through Machine Learning Techniques

Rashid, T. A., Saman, S., & Rezhna (2016) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2016.070724 · Cited by 13

Self-Enforcing Networks (SEN) for the development of (medical) diagnosis systems

Klüver, C. (2016) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2016.7727241 · Cited by 13

Application of neural network based on SIFT local feature extraction in medical image classification

Cui, S., Jiang, H., Wang, Z., & Shen, C. (2017) · International Conference on Image, Vision and Computing (ICIVC) · doi:10.1109/icivc.2017.7984525 · Cited by 13

Prediction and classification of diabetes mellitus using genomic data

Awotunde, J. B., Ayo, F. E., Jimoh, R. G., Ogundokun, R. O., Matiluko, O. E., Oladipo, I. D., & AbdulRaheem, M. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-821187-8.00009-5 · Cited by 14

An Enhanced Novel Dynamic Data Processing (ENDDP) Algorithm for Predicting Heart Disease in Machine Learning

Rao, J. N., & Prasad, R. S. (2020) · International Journal of Scientific Research in Computer Science Engineering and Information Technology · doi:10.32628/cseit206429 · Cited by 13

Machine Learning Based Prediction of Edematous Malnutrition in Afghan Children

Momand, Z., Zarinkhail, M. S., & Aryan, M. F. (2021) · Lecture notes in networks and systems · doi:10.1007/978-3-030-85990-9_20 · Cited by 13

Artificial Neural Network Training using Fireworks Algorithm in Medical Data Mining

Kinkar, R., Kanti, N., & Si, T. (2016) · International Journal of Computer Applications · doi:10.5120/ijca2016908726 · Cited by 13

Feed-forward neural network model for hunger and satiety related VAS score prediction

Krishnan, S., Hendriks, H., Hartvigsen, M. L., & de Graaf, A. A. (2016) · Theoretical Biology and Medical Modelling · doi:10.1186/s12976-016-0043-4 · Cited by 13

Aircraft Air Compressor Bearing Diagnosis Using Discriminant Analysis and Cooperative Genetic Algorithm and Neural Network Approaches

Ouadine, A. Y., Mjahed, M., Ayad, H., & Kari, A. E. (2018) · Applied Sciences · doi:10.3390/app8112243 · Cited by 14

The Modeling of Artificial Neural Network of Early Diagnosis for Malnutrition with Backpropagation Method

Hayat, C., & Abian, B. (2018) · International Conference on Informatics and Computing (ICIC) · doi:10.1109/iac.2018.8780505 · Cited by 13

Deep neural network (DNN) modelling for prediction of the mode of delivery

Kuanar, A., Akbar, A., Pendyala, S., & Kar, D. (2024) · European Journal of Obstetrics & Gynecology and Reproductive Biology · doi:10.1016/j.ejogrb.2024.04.012 · Cited by 12

Ultra-high-sensitive SPR device composed of CaF2 prism, Ag, graphene, and sensing medium for refractive index detection-artificial neural network training

Sassi, I. A., Zbidi, M., & Rhouma, M. B. E. H. (2024) · Optik · doi:10.1016/j.ijleo.2024.172177 · Cited by 12

Machine learning methods for predicting the durability of concrete materials: A review

Zhang, M., & Kang, R. (2025) · Advances in Cement Research · doi:10.1680/jadcr.24.00133 · Cited by 14

A multi

Panagiotou, C., Panagiotakopoulos, T., & Kameas, A. D. (2015) · International Conference on PErvasive Technologies Related to Assistive Environments (PETRA) · doi:10.1145/2769493.2769529 · Cited by 12

Factors influencing the development of visceral metastasis of breast cancer: A retrospective multi-center study

Bartmann, C., Diessner, J., Blettner, M., Häusler, S. F., Janni, W., Kreienberg, R., Krockenberger, M., Schwentner, L., Stein, R. G., Stüber, T. N., et al. (2016) · The Breast · doi:10.1016/j.breast.2016.10.016 · Cited by 12

Usefulness of Artificial Neural Networks in the Diagnosis and Treatment of Sleep Apnea-Hypopnea Syndrome

Álvarez, D., Cerezo-Hernández, A., López-Muñíz, G., de Castro, T. Á., Albi, T. R., Hornero, R., & del Campo, F. (2017) · InTech eBooks · doi:10.5772/66570 · Cited by 13

Development of an Interactive Virtual Reality for Medical Skills Training Supervised by Artificial Neural Network

Esfahlani, S. S., Izsof, V., Minter, S., Kordzadeh, A., Shirvani, H., & Esfahlani, K. S. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-29513-4_34 · Cited by 12

Capabilities of neural network technologies for extracting new medical knowledge and enhancing precise decision making for patients

Yasnitsky, L. N., Артурович, Д. А., Черепанов, Ф. М., Леонидович, Я. В., & Uteva, N. A. (2021) · Expert Review of Precision Medicine and Drug Development · doi:10.1080/23808993.2021.1993595 · Cited by 12

The 4IR-Health Service Delivery Nexus

Niyitunga, E. B. (2023) · International Journal of Public Administration in the Digital Age · doi:10.4018/ijpada.325215 · Cited by 12

A Method for Classification Using Data Mining Technique for Diabetes: A Study of Health Care Information System

Al-Khasawneh, A. (2015) · International Journal of Healthcare Information Systems and Informatics · doi:10.4018/ijhisi.2015070101 · Cited by 12

Entropy Ensemble Filter: A Modified Bootstrap Aggregating (Bagging) Procedure to Improve Efficiency in Ensemble Model Simulation

Foroozand, H., & Weijs, S. V. (2017) · Entropy · doi:10.3390/e19100520 · Cited by 12

PSO-RBFNN: A PSO-Based Clustering Approach for RBFNN Design to Classify Disease Data

Cheruku, R., Edla, D. R., Kuppili, V., & Ramesh, D. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-68612-7_47 · Cited by 12

A Review of Machine Learning for Healthcare Informatics Specifically Tuberculosis Disease Diagnostics

Karmani, P., Chandio, A. A., Korejo, I. A., & Chandio, M. S. (2019) · Communications in computer and information science · doi:10.1007/978-981-13-6052-7_5 · Cited by 12

Comparison of artificial neural network and logistic regression model for factors affecting birth weight

Ki̇ri̇şçi̇, M. (2019) · SN Applied Sciences · doi:10.1007/s42452-019-0391-x · Cited by 14

Differentiation of Clinical Patterns Associated With Rhinologic Disease

Parsel, S. M., Riley, C. A., Todd, C. A., Thomas, A. J., & McCoul, E. D. (2020) · American Journal of Rhinology and Allergy · doi:10.1177/1945892420941706 · Cited by 12

Toward a hemorrhagic trauma severity score: fusing five physiological biomarkers

Bhat, A., Podstawczyk, D. A., Walther, B. K., Aggas, J. R., Machado-Aranda, D., Ward, K. R., & Guiseppi‐Elie, A. (2020) · Journal of Translational Medicine · doi:10.1186/s12967-020-02516-4 · Cited by 12

Artificial Intelligence in Plasma Cell Myeloma: Neural Networks and Support Vector Machines in the Classification of Plasma Cell Myeloma Data at Diagnosis

Yenamandra, A. K., Hughes, C. E., & Maris, A. S. (2021) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_26_21 · Cited by 12

Tissue profiling by nanogold-mediated mass spectrometry and artificial neural networks in the mouse model of human primary hyperoxaluria 1

Houška, J., Peña‐Méndez, E. M., Hernandez-Fernaud, J. R., SALIDO, E. C., Hampl, A., Havel, J., & Vaňhara, P. (2014) · Journal of Applied Biomedicine · doi:10.1016/j.jab.2013.12.001 · Cited by 12

A General Fuzzy Cerebellar Model Neural Network Multidimensional Classifier Using Intuitionistic Fuzzy Sets for Medical Identification

Zhao, J., Lin, L.-Y., & Lin, C. (2016) · Computational Intelligence and Neuroscience · doi:10.1155/2016/8073279 · Cited by 12

Bipolar and Schizophrenia Disorders Diagnosis Using Artificial Neural Network

Fonseca, M. B., de Andrades, R. S., Bach, S. D. L., Wiener, C. D., & Oses, J. P. (2018) · Neuroscience & Medicine · doi:10.4236/nm.2018.94021 · Cited by 12

An Intelligent System for Diagnosing Thyroid Disease in Pregnant Ladies through Artificial Neural NetworkPreprint

Bhatt, V., & Pal, V. K. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3382654 · Cited by 12

An Expert System‐Based Clinical Decision Support System for Hepatitis‐B Prediction & Diagnosis

Panigrahi, N., Ayus, I., & Jena, O. P. (2021) · Wiley eBooks · doi:10.1002/9781119792611.ch4 · Cited by 12

Using artificial intelligence to predict students’ STEM attitudes: an adaptive neural-network-based fuzzy logic model

Körpeoğlu, S. G., & Yıldız, S. G. (2023) · International Journal of Science Education · doi:10.1080/09500693.2023.2269291 · Cited by 11

Neural network Kantorovich operators activated by smooth ramp functions

Agrawal, P. Ν., & Baxhaku, B. (2024) · Mathematical Methods in the Applied Sciences · doi:10.1002/mma.10344 · Cited by 11

Prediction of hidden knowledge from Clinical Database using data mining techniques

Thangarasu, G., & Dominic, P. D. D. (2014) · International Conference on Computer and Information Sciences · doi:10.1109/iccoins.2014.6868414 · Cited by 11

An Artificial Intelligence Approach to Thrombophilia Risk

Vilhena, J., Vicente, H., Martins, M. R., Grañeda, J. M., Caldeira, F., Gusmão, R., Neves, J., & Neves, J. (2017) · International Journal of Reliable and Quality E-Healthcare · doi:10.4018/ijrqeh.2017040105 · Cited by 11

Neural Net Optimization by Weight-Entropy Monitoring

Susan, S., Ranjan, R., Taluja, U., Rai, S., & Agarwal, P. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-981-13-1135-2_16 · Cited by 11

Clinical decision support systems to improve the diagnosis and management of respiratory diseases

Amaral, J. L. M., & de Melo, P. L. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-817133-2.00015-x · Cited by 11

Neural Networks with ESP32 - Are Two Heads Faster than One?

Đokić, K., Martinovic, M., & Radišić, B. (2020) · International Conference on Data Science and Machine Learning Applications (CDMA) · doi:10.1109/cdma47397.2020.00030 · Cited by 11

An Overview of Clinical Decision Support System (CDSS) as a Computational Tool and Its Applications in Public Health

Gupta, P. K., Ramachandran, A. T., Keerthi, A. M., Dave, P. S., Giridhar, S., Kallapur, S. S., & Saikia, A. (2020) · EAI/Springer Innovations in Communication and Computing · doi:10.1007/978-3-030-35280-6_5 · Cited by 11

Development of an Optimized Deep Learning Model for Medical Imaging

Kim, Y. J., & Kim, K. G. (2020) · Journal of the Korean Society of Radiology · doi:10.3348/jksr.2020.0171 · Cited by 11

Early Diagnosis of Alzheimer's Disease using Machine Learning Based Methods

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