RETRACTED ARTICLE: Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda

Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer

Kather, J. N., Pearson, A. T., Halama, N., Jäger, D., Krause, J., Loosen, S. H., Marx, A., Boor, P., Tacke, F., Neumann, U. P., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0462-y · Cited by 1,458 · see citing works

Machine learning and data mining methods in diabetes research

Kavakiotis, I., Tsave, O., Salifoglou, A., Maglaveras, N., Vlahavas, I., & Chouvarda, I. (2017) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2016.12.005 · Cited by 1,445 · see citing works

Artificial Intelligence in Precision Cardiovascular Medicine

Krittanawong, C., Zhang, H., Wang, Z., Aydar, M., & Kitai, T. (2017) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2017.03.571 · Cited by 1,126 · see citing works

Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning

Minaee, S., Kafieh, R., Milan Sonka, A., Yazdani, S., & Soufi, G. J. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101794 · Cited by 1,026 · see citing works

Comparison of Digital Mammography Alone and Digital Mammography Plus Tomosynthesis in a Population-based Screening Program

Skaane, P., Bandos, A. I., Gullien, R., Eben, E. B., Ekseth, U., Haakenaasen, U., Izadi, M., Jebsen, I. N., Jahr, G., Krager, M., et al. (2013) · Radiology · doi:10.1148/radiol.12121373 · Cited by 1,015 · see citing works

Real-time cryo–EM data pre-processing with Warp

Tegunov, D., & Cramer, P. (2018) · Nature Methods · doi:10.1038/s41592-019-0580-y · Cited by 923 · see citing works

Arrhythmia detection using deep convolutional neural network with long duration ECG signals

Yıldırım, Ö., Pławiak, P., Tan, R., & Acharya, U. R. (2018) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2018.09.009 · Cited by 856 · see citing works

Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM

Srinivasu, P. N., SivaSai, J. G., Ijaz, M. F., Bhoi, A. K., Kim, W., & Kang, J. J. (2021) · Sensors · doi:10.3390/s21082852 · Cited by 763 · see citing works

Deep Learning in Alzheimer's Disease: Diagnostic Classification and Prognostic Prediction Using Neuroimaging Data

Jo, T., Nho, K., & Saykin, A. J. (2019) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2019.00220 · Cited by 724 · see citing works

Artificial intelligence in cancer diagnosis and prognosis: Opportunities and challenges

Huang, S., Yang, J., Fong, S. J., & Zhao, Q. (2019) · Cancer Letters · doi:10.1016/j.canlet.2019.12.007 · Cited by 695 · see citing works

Healthfog: an ensemble deep learning based smart healthcare system for automatic diagnosis of heart diseases in integrated iot and fog computing environments

Tuli, S., Basumatary, N., Gill, S. S., Kahani, M., Arya, R. C., Wander, G. S., & Buyya, R. (2020) · Future Generation Computer Systems · doi:10.1016/j.future.2019.10.043 · Cited by 669 · cited 1× · see citing works

A Hybrid Intelligent System Framework for the Prediction of Heart Disease Using Machine Learning Algorithms

Haq, A. U., Li, J. P., Memon, M. H., Nazir, S., & Sun, R. (2018) · Mobile Information Systems · doi:10.1155/2018/3860146 · Cited by 621 · see citing works

Diabetes Prediction using Machine Learning Algorithms

Mujumdar, A. M., & Vaidehi, V. (2019) · Procedia Computer Science · doi:10.1016/j.procs.2020.01.047 · Cited by 560 · see citing works

A novel machine learning-derived radiotranscriptomic signature of perivascular fat improves cardiac risk prediction using coronary CT angiography

Oikonomou, E. K., Williams, M. C., Kotanidis, C. P., Desai, M. Y., Marwan, M., Antonopoulos, A. S., Thomas, K. E., Thomas, S., Akoumianakis, I., Fan, L. M., et al. (2019) · European Heart Journal · doi:10.1093/eurheartj/ehz592 · Cited by 534 · see citing works

Structure–Relaxivity Relationships of Magnetic Nanoparticles for Magnetic Resonance Imaging

Zhou, Z., Yang, L., Gao, J., & Chen, X. S. (2019) · Advanced Materials · doi:10.1002/adma.201804567 · Cited by 488 · see citing works

Automated diabetic retinopathy detection in smartphone-based fundus photography using artificial intelligence

Rajalakshmi, R., Subashini, R., Anjana, R. M., & Mohan, V. (2018) · Eye · doi:10.1038/s41433-018-0064-9 · Cited by 462 · see citing works

Applications of Machine Learning Predictive Models in the Chronic Disease Diagnosis

Battineni, G., Sagaro, G. G., Chintalapudi, N., & Amenta, F. (2020) · Journal of Personalized Medicine · doi:10.3390/jpm10020021 · Cited by 418 · see citing works

Real-time artificial intelligence for detection of upper gastrointestinal cancer by endoscopy: a multicentre, case-control, diagnostic study

Luo, H., Xu, G., Li, C., He, L., Luo, L., Wang, Z., Jing, B., Deng, Y., Jin, Y., Yin, L., et al. (2019) · The Lancet Oncology · doi:10.1016/s1470-2045(19)30637-0 · Cited by 417 · see citing works

Artificial Intelligence–Based Breast Cancer Nodal Metastasis Detection: Insights Into the Black Box for Pathologists

Liu, Y., Kohlberger, T., Norouzi, M., Dahl, G. E., Smith, J. L., Mohtashamian, A., Olson, N. H., Peng, L. H., Hipp, J., & Stumpe, M. C. (2018) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2018-0147-oa · Cited by 402 · see citing works

Feature selection and classification systems for chronic disease prediction: A review

Jain, D. K., & Singh, V. P. (2018) · Egyptian Informatics Journal · doi:10.1016/j.eij.2018.03.002 · Cited by 369 · see citing works

Prediction of Type 2 Diabetes using Machine Learning Classification Methods

Tigga, N. P., & Garg, S. (2020) · Procedia Computer Science · doi:10.1016/j.procs.2020.03.336 · Cited by 363 · see citing works

Data-driven modeling and prediction of blood glucose dynamics: Machine learning applications in type 1 diabetes

Woldaregay, A. Z., Årsand, E., Walderhaug, S., Albers, D., Mamykina, L., Botsis, T., & Hartvigsen, G. (2019) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2019.07.007 · Cited by 358 · see citing works

An IoT Framework for Heart Disease Prediction Based on MDCNN Classifier

Khan, M. A. (2020) · IEEE Access · doi:10.1109/access.2020.2974687 · Cited by 346 · see citing works

Diabetes detection using deep learning algorithms

Swapna, G., Vinayakumar, R., & Soman, K. P. (2018) · ICT Express · doi:10.1016/j.icte.2018.10.005 · Cited by 345 · see citing works

An augmented reality microscope with real-time artificial intelligence integration for cancer diagnosis

Chen, P.-H. C., Gadepalli, K., MacDonald, R., Liu, Y., Kadowaki, S., Nagpal, K., Kohlberger, T., Dean, J., Corrado, G. S., Hipp, J. D., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0539-7 · Cited by 342 · see citing works

Expert-Level Diagnosis of Nonpigmented Skin Cancer by Combined Convolutional Neural Networks

Tschandl, P., Rosendahl, C. O., Akay, B. N., Argenziano, G., Blum, A., Braun, R. P., Cabo, H., Gourhant, J.-Y., Kreusch, J. F., Lallas, A., et al. (2018) · JAMA Dermatology · doi:10.1001/jamadermatol.2018.4378 · Cited by 327 · see citing works

Classification and Visualization of Alzheimer’s Disease using Volumetric Convolutional Neural Network and Transfer Learning

Oh, K., Chung, Y., Kim, K. W., Kim, W., & Oh, I.-S. (2019) · Scientific Reports · doi:10.1038/s41598-019-54548-6 · Cited by 318 · see citing works

Deep learning in ultrasound imaging

van Sloun, R. J. G., Cohen, R., & Eldar, Y. C. (2020) · Proceedings of the IEEE · doi:10.1109/jproc.2019.2932116 · Cited by 316 · see citing works

The Effect of Image Resolution on Deep Learning in Radiography

Sabottke, C. F., & Spieler, B. M. (2020) · Radiology: Artificial Intelligence · doi:10.1148/ryai.2019190015 · Cited by 312 · see citing works

Using Artificial Intelligence to Reduce the Risk of Nonadherence in Patients on Anticoagulation Therapy

Labovitz, D. L., Shafner, L., Reyes Gil, M., Virmani, D., & Hanina, A. (2017) · Stroke · doi:10.1161/strokeaha.116.016281 · Cited by 311 · see citing works

Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis

Yue, W., Wang, Z., Chen, H., Payne, A. M., & Liu, X. (2018) · Designs · doi:10.3390/designs2020013 · Cited by 306 · see citing works

Detection and diagnosis of chronic kidney disease using deep learning-based heterogeneous modified artificial neural network

Ma, F., Sun, T., Liu, L., & Jing, H. (2020) · Future Generation Computer Systems · doi:10.1016/j.future.2020.04.036 · Cited by 277 · see citing works

Machine learning and artificial intelligence based Diabetes Mellitus detection and self-management: A systematic review

Chaki, J., Ganesh, S. T., Cidham, S. K., & Theertan, S. A. (2020) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2020.06.013 · Cited by 272 · see citing works

Data-Driven Cervical Cancer Prediction Model with Outlier Detection and Over-Sampling Methods

Ijaz, M. F., Attique, M., & Son, Y. (2020) · Sensors · doi:10.3390/s20102809 · Cited by 268 · see citing works

A Personalized Healthcare Monitoring System for Diabetic Patients by Utilizing BLE-Based Sensors and Real-Time Data Processing

Alfian, G., Syafrudin, M., Ijaz, M. F., Syaekhoni, M. A., Fitriyani, N. L., & Rhee, J. (2018) · Sensors · doi:10.3390/s18072183 · Cited by 257 · see citing works

Heart Disease Detection by Using Machine Learning Algorithms and a Real-Time Cardiovascular Health Monitoring System

Nashif, S., Raihan, M. R., Islam, M. R., & Imam, H. (2018) · World Journal of Engineering and Technology · doi:10.4236/wjet.2018.64057 · Cited by 242 · see citing works

Management of Hypertension in the Digital Era

Kario, K. (2020) · Hypertension · doi:10.1161/hypertensionaha.120.14742 · Cited by 240 · see citing works

Moderate Carotid Artery Stenosis: MR Imaging–depicted Intraplaque Hemorrhage Predicts Risk of Cerebrovascular Ischemic Events in Asymptomatic Men

Singh, N., Moody, A. R., Gladstone, D. J., Leung, G., Ravikumar, R., Zhan, J. Q., & Maggisano, R. (2009) · Radiology · doi:10.1148/radiol.2522080792 · Cited by 228 · see citing works

Hybrid Prediction Model for Type 2 Diabetes and Hypertension Using DBSCAN-Based Outlier Detection, Synthetic Minority Over Sampling Technique (SMOTE), and Random Forest

Ijaz, M. F., Alfian, G., Syafrudin, M., & Rhee, J. (2018) · Applied Sciences · doi:10.3390/app8081325 · Cited by 220 · see citing works

Applying Machine Learning in Liver Disease and Transplantation: A Comprehensive Review

Spann, A., Yasodhara, A., Kang, J., Watt, K. D. S., Wang, B., Goldenberg, A., & Bhat, M. (2020) · Hepatology · doi:10.1002/hep.31103 · Cited by 202 · see citing works

Deep learning based liver cancer detection using watershed transform and Gaussian mixture model techniques

Das, A., Acharya, U. R., Panda, S. S., & Sabut, S. (2018) · Cognitive Systems Research · doi:10.1016/j.cogsys.2018.12.009 · Cited by 200 · see citing works

Artificial Intelligence in Nuclear Medicine

Nensa, F., Demircioğlu, A., & Rischpler, C. (2019) · Journal of Nuclear Medicine · doi:10.2967/jnumed.118.220590 · Cited by 197 · see citing works

Deep Convolution Neural Network Based System for Early Diagnosis of Alzheimer's Disease

Janghel, R. R., & Rathore, Y. K. (2020) · IRBM · doi:10.1016/j.irbm.2020.06.006 · Cited by 197 · see citing works

Augmented Bladder Tumor Detection Using Deep Learning

Shkolyar, E., Jia, X., Chang, T., Trivedi, D. R., Mach, K. E., Meng, M. Q. ‐., Xing, L., & Liao, J. C. (2019) · European Urology · doi:10.1016/j.eururo.2019.08.032 · Cited by 196 · see citing works

The emerging role of cognitive computing in healthcare: a systematic literature review

Behera, R. K., Bala, P. K., & Dhir, A. (2019) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2019.04.024 · Cited by 193 · see citing works

Current status of artificial intelligence applications in urology and their potential to influence clinical practice

Chen, J., Remulla, D. B., Nguyen, J. H., Dua, A., Liu, Y., Prokar Dasgupta, A., & Hung, A. J. (2019) · British Journal of Urology · doi:10.1111/bju.14852 · Cited by 187 · see citing works

Kidney disease detection and segmentation using artificial neural network and multi-kernel k-means clustering for ultrasound images

Nithya, A., Appathurai, A., Venkatadri, N., Ramji, D. R., & Palagan, C. A. (2019) · Measurement · doi:10.1016/j.measurement.2019.106952 · Cited by 181 · see citing works

A new machine learning method for identifying Alzheimer's disease

Liu, L., Zhao, S., Chen, H., & Wang, A. (2019) · Simulation Modelling Practice and Theory · doi:10.1016/j.simpat.2019.102023 · Cited by 172 · see citing works

Gastrointestinal diseases segmentation and classification based on duo-deep architectures

Khan, M. A., Khan, M. A., Ahmed, F., Mittal, M., Goyal, L. M., Hemanth, D. J., & Satapathy, S. C. (2019) · Pattern Recognition Letters · doi:10.1016/j.patrec.2019.12.024 · Cited by 168 · see citing works

Data-Driven Blood Glucose Pattern Classification and Anomalies Detection: Machine-Learning Applications in Type 1 Diabetes

Woldaregay, A. Z., Årsand, E., Botsis, T., Albers, D. J., Mamykina, L., & Hartvigsen, G. (2019) · Journal of Medical Internet Research · doi:10.2196/11030 · Cited by 164 · see citing works

Diabetes Mellitus Affected Patients Classification and Diagnosis through Machine Learning Techniques

Mercaldo, F., Nardone, V., & Santone, A. (2017) · Procedia Computer Science · doi:10.1016/j.procs.2017.08.193 · Cited by 161 · see citing works

Deep convolutional neural networks for classification of mild cognitive impaired and Alzheimer's disease patients from scalp EEG recordings

Morabito, F. C., Campolo, M., Ieracitano, C., Ebadi, M. J., Bonanno, L., Bramanti, A., Salvo, S. D., Mammone, N., & Bramanti, P. (2016) · IEEE International Forum on Research and Technology for Society and Industry (RTSI) · doi:10.1109/rtsi.2016.7740576 · Cited by 154 · see citing works

Machine learning prediction of incidence of Alzheimer’s disease using large-scale administrative health data

Park, J. H., Cho, H. E., Kim, J. H., Wall, M. M., Stern, Y., Lim, H., Yoo, S., Kim, H. S., & Cha, J. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0256-0 · Cited by 151 · see citing works

Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications

Chui, K. T., Alhalabi, W., Pang, S. S. H., de Pablos, P. O., Liu, R. W., & Zhao, M. (2017) · Sustainability · doi:10.3390/su9122309 · Cited by 147 · see citing works

Automatic detection of tuberculosis bacilli from microscopic sputum smear images using deep learning methods

Panicker, R. O., Kalmady, K. S., Rajan, J., & Sabu, M. K. (2018) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2018.05.007 · Cited by 146 · see citing works

A new approach for classification skin lesion based on transfer learning, deep learning, and IoT system

Rodrigues, D. D. A., Ivo, R. F., Satapathy, S. C., Wang, S., Hemanth, D. J., & Filho, P. P. R. (2020) · Pattern Recognition Letters · doi:10.1016/j.patrec.2020.05.019 · Cited by 146 · see citing works

Prediction of Coronary Heart Disease using Machine Learning

Gonsalves, A. H., Thabtah, F., Mohammad, R. M. A., & Singh, G. (2019) · International Conference on Deep Learning Technologies (ICDLT) · doi:10.1145/3342999.3343015 · Cited by 145 · see citing works

Diagnosis of skin diseases using Convolutional Neural Networks

Rathod, J., Waghmode, V., Sodha, A., & Bhavathankar, P. (2018) · International Conference on Electronics, Communication and Aerospace Technology (ICECA) · doi:10.1109/iceca.2018.8474593 · Cited by 139 · see citing works

Medical Internet of things using machine learning algorithms for lung cancer detection

Pradhan, K. S., & Chawla, P. (2020) · Journal of Management Analytics · doi:10.1080/23270012.2020.1811789 · Cited by 138 · see citing works

Improving the Diagnosis of Liver Disease Using Multilayer Perceptron Neural Network and Boosted Decision Trees

Abdar, M., Yen, N. Y., & Hung, J. C.-S. (2017) · Journal of Medical and Biological Engineering · doi:10.1007/s40846-017-0360-z · Cited by 134 · see citing works

Hippocampal atrophy based Alzheimer’s disease diagnosis via machine learning methods

Uysal, G., & Öztürk, M. (2020) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2020.108669 · Cited by 131 · see citing works

Liver Disease Prediction by Using Different Decision Tree Techniques

Nahar, N., & Ara, F. (2018) · International Journal of Data Mining & Knowledge Management Process · doi:10.5121/ijdkp.2018.8201 · Cited by 130 · see citing works

Machine learning and treatment outcome prediction for oral cancer

Chu, C. S., Lee, N. P. Y., Adeoye, J., Thomson, P. J., & Choi, S. W. (2020) · Journal of Oral Pathology and Medicine · doi:10.1111/jop.13089 · Cited by 128 · see citing works

Supervised machine learning based gait classification system for early detection and stage classification of Parkinson’s disease

Balaji, E., Brindha, D., & Balakrishnan, R. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106494 · Cited by 126 · see citing works

Highly precise risk prediction model for new‐onset hypertension using artificial intelligence techniques

Kanegae, H., Suzuki, K. S., Fukatani, K., Ito, T., Harada, N., & Kario, K. (2019) · Journal of Clinical Hypertension · doi:10.1111/jch.13759 · Cited by 121 · see citing works

Comparing the performance of artificial intelligence and conventional diagnosis criteria for detecting left ventricular hypertrophy using electrocardiography

Kwon, J., Jeon, K., Kim, H. M., Kim, M. J., Lim, S. M., Kim, K., Song, P. S., Park, J., Choi, R. K., & Oh, B. (2019) · EP Europace · doi:10.1093/europace/euz324 · Cited by 119 · see citing works

Iot based patient monitoring and diagnostic prediction tool using ensemble classifier

Ani, R., Krishna, S., Anju, N., Aslam, M. S., & Deepa, O. S. (2017) · International Conference on Advances in Computing, Communications and Informatics (ICACCI) · doi:10.1109/icacci.2017.8126068 · Cited by 117 · see citing works

Predictive modelling and analytics for diabetes using a machine learning approach

Kaur, H., & Kumari, V. (2020) · Applied Computing and Informatics · doi:10.1016/j.aci.2018.12.004 · Cited by 115 · see citing works

Comprehensive characterisation of hypertensive heart disease left ventricular phenotypes

Rodrigues, J. C. L., Amadu, A. M., Dastidar, A. G., Szantho, G., Lyen, S. M., Godsave, C., Ratcliffe, L. E. K., Burchell, A. E., Hart, E. C., Hamilton, M. C. K., et al. (2016) · Heart · doi:10.1136/heartjnl-2016-309576 · Cited by 114 · see citing works

Towards large-scale case-finding: training and validation of residual networks for detection of chronic obstructive pulmonary disease using low-dose CT

Tang, L. Y. W., Coxson, H. O., Lam, S. C., Leipsic, J. A., Tam, R. C., & Sin, D. D. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30064-9 · Cited by 111 · see citing works

Automatic detection of tuberculosis related abnormalities in Chest X-ray images using hierarchical feature extraction scheme

Chandra, T. B., Verma, K., Singh, B. K., Jain, D., & Netam, S. S. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113514 · Cited by 109 · see citing works

Using machine learning on cardiorespiratory fitness data for predicting hypertension: The Henry Ford ExercIse Testing (FIT) Project

Sakr, S., Elshawi, R., Ahmed, A. M., Qureshi, W., Brawner, C. A., Keteyian, S. J., Blaha, M. J., & Al-Mallah, M. H. (2018) · PLoS ONE · doi:10.1371/journal.pone.0195344 · Cited by 108 · see citing works

Aiding the Diagnosis of Diabetic and Hypertensive Retinopathy Using Artificial Intelligence-Based Semantic Segmentation

Arsalan, M., Owais, M., Mahmood, T., Cho, S. W., & Park, K. R. (2019) · Journal of Clinical Medicine · doi:10.3390/jcm8091446 · Cited by 108 · see citing works

Deep learning for automated classification of tuberculosis-related chest X-Ray: dataset distribution shift limits diagnostic performance generalizability

Sathitratanacheewin, S., Sunanta, P., & Pongpirul, K. (2020) · Heliyon · doi:10.1016/j.heliyon.2020.e04614 · Cited by 108 · see citing works

Deep and joint learning of longitudinal data for Alzheimer's disease prediction

Lei, B., Yang, M., Yang, P., Zhou, F., Wen, H., Zou, W., Li, X., Wang, T., Xiao, X., & Wang, S. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107247 · Cited by 105 · see citing works

A multiple multilayer perceptron neural network with an adaptive learning algorithm for thyroid disease diagnosis in the internet of medical things

Hosseinzadeh, M., Ahmed, O. H., Ghafour, M. Y., Safara, F., Hama, H. K., Ali, S., Vo, B., & Chiang, H. (2020) · The Journal of Supercomputing · doi:10.1007/s11227-020-03404-w · Cited by 105 · see citing works

The Current Research Landscape of the Application of Artificial Intelligence in Managing Cerebrovascular and Heart Diseases: A Bibliometric and Content Analysis

Tran, B. X., Latkin, C. A., Vu, G. T., Nguyen, H. L. T., Nghiem, S. H., Tan, M.-X., Lim, Z. K., Ho, C. S. H., & Ho, R. (2019) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph16152699 · Cited by 101 · see citing works

An Improved Multi-Modal based Machine Learning Approach for the Prognosis of Alzheimer’s disease

Khan, A., & Zubair, S. (2020) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2020.04.004 · Cited by 101 · see citing works

Artificial Intelligence-Based Classification of Multiple Gastrointestinal Diseases Using Endoscopy Videos for Clinical Diagnosis

Owais, M., Arsalan, M., Choi, J., Mahmood, T., & Park, K. R. (2019) · Journal of Clinical Medicine · doi:10.3390/jcm8070986 · Cited by 99 · see citing works

Prediction of thyroid disease using decision tree ensemble method

Yadav, D. C., & Pal, S. (2020) · Human-Intelligent Systems Integration · doi:10.1007/s42454-020-00006-y · Cited by 99 · see citing works

Future Direction for Using Artificial Intelligence to Predict and Manage Hypertension

Krittanawong, C., Bomback, A. S., Baber, U., Bangalore, S., Messerli, F. H., & Tang, W. H. W. (2018) · Current Hypertension Reports · doi:10.1007/s11906-018-0875-x · Cited by 95 · see citing works

Retinal specialist versus artificial intelligence detection of retinal fluid from OCT

Keenan, T. D. L., Clemons, T. E., Domalpally, A., Elman, M. J., Havilio, M., Agrón, E., Benyamini, G., & Chew, E. Y. (2021) · Ophthalmology · doi:10.1016/j.ophtha.2020.06.038 · Cited by 93 · see citing works

Federated Learning Systems for Healthcare: Perspective and Recent Progress

Kumar, Y., & Singla, R. P. (2021) · Studies in computational intelligence · doi:10.1007/978-3-030-70604-3_6 · Cited by 93 · see citing works

Classification of Skin Disease using Ensemble Data Mining Techniques

Verma, A. K., Pal, S., & Kumar, S. (2019) · Asian Pacific Journal of Cancer Prevention · doi:10.31557/apjcp.2019.20.6.1887 · Cited by 92 · see citing works

Artificial Intelligence and Hypertension: Recent Advances and Future Outlook

Chaikijurajai, T., Laffin, L. J., & Tang, W. H. W. (2020) · American Journal of Hypertension · doi:10.1093/ajh/hpaa102 · Cited by 92 · see citing works

Diagnosis and monitoring of Alzheimer's patients using classical and deep learning techniques

Raza, M., Awais, M., Ellahi, W., Aslam, N., Nguyen, H. X., & Minh, H. L. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.06.038 · Cited by 90 · see citing works

Diagnosis of Alzheimer's Disease Using Machine Learning

Lodha, P., Talele, A. K., & Degaonkar, K. (2018) · International Conference on Computing Communication Control and automation · doi:10.1109/iccubea.2018.8697386 · Cited by 85 · see citing works

Analysis of tuberculosis severity levels from CT pulmonary images based on enhanced residual deep learning architecture

Gao, X. W., James-Reynolds, C., & Currie, E. (2019) · Neurocomputing · doi:10.1016/j.neucom.2018.12.086 · Cited by 83 · see citing works

Diabetic retinopathy detection through novel tetragonal local octa patterns and extreme learning machines

Nazir, T., Irtaza, A., Shabbir, Z., Javed, A., Akram, U., & Mahmood, M. T. (2019) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2019.07.003 · Cited by 78 · see citing works

Advancing Alzheimer’s research: a review of big data promises

Zhang, R., Simon, G., & Yu, F. (2017) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2017.07.002 · Cited by 77 · see citing works

Soft Clustering for Enhancing the Diagnosis of Chronic Diseases over Machine Learning Algorithms

Aldhyani, T. H. H., Alshebami, A. S., & Alzahrani, M. Y. (2020) · Journal of Healthcare Engineering · doi:10.1155/2020/4984967 · Cited by 76 · see citing works

Case-based reasoning support for liver disease diagnosis

Chuang, C. (2011) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2011.06.002 · Cited by 74 · see citing works

Two-stage artificial intelligence model for jointly measurement of atherosclerotic wall thickness and plaque burden in carotid ultrasound: A screening tool for cardiovascular/stroke risk assessment

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Internet of Things, Digital Biomarker, and Artificial Intelligence in Spine: Current and Future Perspectives

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Medical Diagnostic Systems Using Artificial Intelligence (AI) Algorithms: Principles and Perspectives

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