ARCHIVES
Year 2026 · Volume 6 · Issue 5
Review Article
Machine Learning and Deep Learning for Medical Diagnosis: A Modality-Wise Review of Clinical Records, Biosignals and Medical Images
Pankaj Kumar1
T. John Paul Antony2
1 Department of Information Science and Engineering, Acharya Institute of Technology, Bengaluru, Karnataka India. 2 Department of Computer Science (Artificial Intelligence), The American College, Madurai, Tamilnadu, India.
Published Online: September-October 2026
Pages: 425-429
Cite this article
↗ https://www.doi.org/10.59256/ijrtmr.20260605046
References
1. Esteva, A. Robicquet, B. Ramsundar, V. Kuleshov, M. DePristo, K. Chou, C. Cui, G. Corrado, S. Thrun, and J. Dean, “A guide to deep learning in healthcare,” Nat. Med., vol. 25, no. 1, pp. 24–29, 2019, doi: 10.1038/s41591-018-0316-z.
2. Rajkomar, J. Dean, and I. Kohane, “Machine learning in medicine,” N. Engl. J. Med., vol. 380, no. 14, pp. 1347–1358, 2019, doi: 10.1056/NEJMra1814259.
3. J. M. Johnson and T. M. Khoshgoftaar, “Survey on deep learning with class imbalance,” J. Big Data, vol. 6, no. 1, p. 27, 2019, doi: 10.1186/s40537-019-0192-5.
4. E. J. Topol, “High-performance medicine: The convergence of human and artificial intelligence,” Nat. Med., vol. 25, no.1, pp. 44–56, 2019, doi: 10.1038/s41591-018-0300-7.
5. M. S. Hema, R. Maheshprabhu, K. S. Reddy, M. N. Guptha, and V. Pandimurugan, “Prediction analysis for Parkinson disease using multiple feature selection & classification methods,” Multimed. Tools Appl., vol. 82, no. 27, pp. 42995–43012, 2023.
6. S. Uddin, A. Khan, M. E. Hossain, and M. A. Moni, “Comparing different supervised machine learning algorithms for disease prediction,” BMC Med. Inform. Decis. Mak., vol. 19, no. 1, p. 281, 2019, doi: 10.1186/s12911-019-1004-8.
7. T. Margaret Mary, V. S. Prakash, K. Nagarajan, and K. S. Divya, “Breast cancer analysis in R toprognosis and prediction cancer diagnosis based on cell features,” AIP Conf. Proc., vol. 3175, no. 1, p. 020074, 2025, doi: 10.1063/5.0260540.
8. S. Pandikumar, S. Premkumar, T. Guhan, T. Margaret Mary, M. Kannan, and V. Pream Sudha, “An implementation of enhanced Inception-Residual convolutional neural network in lung cancer prediction,” J. Theor. Appl. Inf. Technol., vol. 103, no. 19, pp. 7755–7766, 2025.
9. S. Mohan, C. Thirumalai, and G. Srivastava, “Effective heart disease prediction using hybrid machine learning techniques,” IEEE Access, vol. 7, pp. 81542–81554, 2019, doi: 10.1109/ACCESS.2019.2923707.
10. S. Pandikumar, H. R. Jakaraddi, C. Menaka, and A. R. Nambiar, “DermAI: A triple-CNN ensemble learning approach for early detection of skin tone abnormalities in clinical settings,” in Proc. Int. Conf. Sustain. Comput. Netw. Appl. (ICSCNA), Theni, India, 2024, pp. 1621–1626, doi: 10.1109/ICSCNA63714.2024.10864008.
11. M. J. Page et al., “The PRISMA 2020 statement: An updated guideline for reporting systematic reviews,” BMJ, vol. 372,p. n71, 2021, doi: 10.1136/bmj.n71.
12. K. B. S. Prakash, M. Sudharsan, G. Meenakshi, and T. Margaret Mary, “Engaging coronary heart disease prediction (Vein) through Feature U-Net improvement with ensemble learning based hybrid bagging and boosting techniques,” in Communications in Computer and Information Science, vol. 2427. Cham, Switzerland: Springer, 2025, pp. 348–374, doi: 10.1007/978-3-031-86299-1_25.
13. S. M. Lundberg et al., “From local explanations to global understanding with explainable AI for trees,” Nat. Mach. Intell., vol. 2, no. 1, pp. 56–67, 2020, doi: 10.1038/s42256-019-0138-9.
14. T. Margaret Mary, V. S. Prakash, K. S. Divya, and A. George, “Hybrid ML algorithms for learning disability forecast in school going children using Python in machine learning techniques,” in Proc. 7th Int. Conf. Electronics, Communication and Aerospace Technology (ICECA), 2023, pp. 1043–1049, doi: 10.1109/ICECA58529.2023.10395259.
15. T. Margaret Mary and M. Hanumanthappa, “Hybrid classification approach HDLMM for learning disability prediction in school going children using data mining technique,” J. Theor. Appl. Inf. Technol., vol. 95, no. 13, pp. 2989–2998, 2017.
16. Rudin, “Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead,” Nat. Mach. Intell., vol. 1, no. 5, pp. 206–215, 2019, doi: 10.1038/s42256-019-0048-x.
17. T. Margaret Mary, K. Soumya, G. Ramanathan, and G. Clinton, “An assessment on classification in Python using data science,” in Proc. 3rd Int. Conf. Inventive Research in Computing Applications (ICIRCA), 2021, pp. 551–555.
18. L. Sathish Kumar, S. Routray, A. V. Prabu, S. Rajasoundaran et al., “Artificial intelligence based health indicator extraction and disease symptoms identification using medical hypothesis models,” Cluster Comput., vol. 26, no. 4, pp. 2325–2337, 2023.
19. Y. A. Qadri, A. Nauman, Y. B. Zikria, A. V. Vasilakos, and S. W. Kim, “The future of healthcare Internet of Things: A survey of emerging technologies,” IEEE Commun. Surveys Tuts., vol. 22, no. 2, pp. 1121–1167, 2020, doi: 10.1109/COMST.2020.2973314.
20. Jamunadevi, P. Ragupathy, P. Sritha, S. Pandikumar, and S. Deepa, “Performance analysis of random forest classifier in extracting features from the EEG signal,” in Proc. Int. Conf. Adv. Comput. Technol. Appl. (ICACTA), Coimbatore, India, 2022, pp. 1–5, doi: 10.1109/ICACTA54488.2022.9753364
21. T. Margaret Mary, G. Ramanathan, A. Sangamithra, and K. Soumya, “Design and enactment of heart attack deduction using IoT measuring device,” J. Phys.: Conf. Ser., vol. 1427, no. 1, p. 012014, 2020, doi: 10.1088/1742-6596/1427/1/012014.
22. S. K. Zhou, H. Greenspan, C. Davatzikos, J. S. Duncan, B. van Ginneken, A. Madabhushi, J. L. Prince, D. Rueckert, and R. M. Summers, “A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises,” Proc. IEEE, vol. 109, no. 5, pp. 820–838, 2021, doi: 10.1109/JPROC.2021.3054390.
23. V. Pandimurugan, S. Ahmad, A. V. Prabu, M. K. I. Rahmani, H. A. M. Abdeljaber et al., “CNN-based deep learning model for early identification and categorization of melanoma skin cancer using medical imaging,” SN Comput. Sci., vol. 5, no. 7, p. 911, 2024, doi: 10.1007/s42979-024-03270-w.
24. L. S. Kumar, S. Hariharasitaraman, K. Narayanasamy, K. Thinakaran et al., “AlexNet approach for early stage Alzheimer’s disease detection from MRI brain images,” Mater. Today: Proc., vol. 51, pp. 58–65, 2022.
25. M. Tan and Q. V. Le, “EfficientNet: Rethinking model scaling for convolutional neural networks,” in Proc. 36th Int.Conf. Mach. Learn. (ICML), 2019, pp. 6105–6114.
26. L. Sathish Kumar, A. V. Prabu, V. Pandimurugan, S. Rajasoundaran et al., “A comparative experimental analysis and deep evaluation practices on human bone fracture detection using X-ray images,” Concurrency Comput.: Pract. Exper.,p. e7307, 2022.
27. J. C, P. V, A. P. D, P. S, and M. Manimaran, “Naive Bayes and SVM-based classification of myocardial tissue with echocardiography images for heart disease diagnosis,” in Proc. Int. Conf. Sustain. Comput. Netw. Appl. (ICSCNA), Theni, India, 2023, pp. 618–625, doi: 10.1109/ICSCNA58489.2023.10370212.
28. P. Kairouz et al., “Advances and open problems in federated learning,” Found. Trends Mach. Learn., vol. 14, no. 1–2, pp. 1–210, 2021, doi: 10.1561/2200000083.
29. S. Pandikumar, V. Pandimurugan, A. Rama Prasath, S. Karattil, S. C. B. Jaganathan, and P. Vigneshwaran, “A novel framework for differential privacy based federated continual learning for dynamic medical data analysis,” SN Comput. Sci., vol. 7, no. 6, p. 664, 2026, doi: 10.1007/s42979-026-05245-5.
2. Rajkomar, J. Dean, and I. Kohane, “Machine learning in medicine,” N. Engl. J. Med., vol. 380, no. 14, pp. 1347–1358, 2019, doi: 10.1056/NEJMra1814259.
3. J. M. Johnson and T. M. Khoshgoftaar, “Survey on deep learning with class imbalance,” J. Big Data, vol. 6, no. 1, p. 27, 2019, doi: 10.1186/s40537-019-0192-5.
4. E. J. Topol, “High-performance medicine: The convergence of human and artificial intelligence,” Nat. Med., vol. 25, no.1, pp. 44–56, 2019, doi: 10.1038/s41591-018-0300-7.
5. M. S. Hema, R. Maheshprabhu, K. S. Reddy, M. N. Guptha, and V. Pandimurugan, “Prediction analysis for Parkinson disease using multiple feature selection & classification methods,” Multimed. Tools Appl., vol. 82, no. 27, pp. 42995–43012, 2023.
6. S. Uddin, A. Khan, M. E. Hossain, and M. A. Moni, “Comparing different supervised machine learning algorithms for disease prediction,” BMC Med. Inform. Decis. Mak., vol. 19, no. 1, p. 281, 2019, doi: 10.1186/s12911-019-1004-8.
7. T. Margaret Mary, V. S. Prakash, K. Nagarajan, and K. S. Divya, “Breast cancer analysis in R toprognosis and prediction cancer diagnosis based on cell features,” AIP Conf. Proc., vol. 3175, no. 1, p. 020074, 2025, doi: 10.1063/5.0260540.
8. S. Pandikumar, S. Premkumar, T. Guhan, T. Margaret Mary, M. Kannan, and V. Pream Sudha, “An implementation of enhanced Inception-Residual convolutional neural network in lung cancer prediction,” J. Theor. Appl. Inf. Technol., vol. 103, no. 19, pp. 7755–7766, 2025.
9. S. Mohan, C. Thirumalai, and G. Srivastava, “Effective heart disease prediction using hybrid machine learning techniques,” IEEE Access, vol. 7, pp. 81542–81554, 2019, doi: 10.1109/ACCESS.2019.2923707.
10. S. Pandikumar, H. R. Jakaraddi, C. Menaka, and A. R. Nambiar, “DermAI: A triple-CNN ensemble learning approach for early detection of skin tone abnormalities in clinical settings,” in Proc. Int. Conf. Sustain. Comput. Netw. Appl. (ICSCNA), Theni, India, 2024, pp. 1621–1626, doi: 10.1109/ICSCNA63714.2024.10864008.
11. M. J. Page et al., “The PRISMA 2020 statement: An updated guideline for reporting systematic reviews,” BMJ, vol. 372,p. n71, 2021, doi: 10.1136/bmj.n71.
12. K. B. S. Prakash, M. Sudharsan, G. Meenakshi, and T. Margaret Mary, “Engaging coronary heart disease prediction (Vein) through Feature U-Net improvement with ensemble learning based hybrid bagging and boosting techniques,” in Communications in Computer and Information Science, vol. 2427. Cham, Switzerland: Springer, 2025, pp. 348–374, doi: 10.1007/978-3-031-86299-1_25.
13. S. M. Lundberg et al., “From local explanations to global understanding with explainable AI for trees,” Nat. Mach. Intell., vol. 2, no. 1, pp. 56–67, 2020, doi: 10.1038/s42256-019-0138-9.
14. T. Margaret Mary, V. S. Prakash, K. S. Divya, and A. George, “Hybrid ML algorithms for learning disability forecast in school going children using Python in machine learning techniques,” in Proc. 7th Int. Conf. Electronics, Communication and Aerospace Technology (ICECA), 2023, pp. 1043–1049, doi: 10.1109/ICECA58529.2023.10395259.
15. T. Margaret Mary and M. Hanumanthappa, “Hybrid classification approach HDLMM for learning disability prediction in school going children using data mining technique,” J. Theor. Appl. Inf. Technol., vol. 95, no. 13, pp. 2989–2998, 2017.
16. Rudin, “Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead,” Nat. Mach. Intell., vol. 1, no. 5, pp. 206–215, 2019, doi: 10.1038/s42256-019-0048-x.
17. T. Margaret Mary, K. Soumya, G. Ramanathan, and G. Clinton, “An assessment on classification in Python using data science,” in Proc. 3rd Int. Conf. Inventive Research in Computing Applications (ICIRCA), 2021, pp. 551–555.
18. L. Sathish Kumar, S. Routray, A. V. Prabu, S. Rajasoundaran et al., “Artificial intelligence based health indicator extraction and disease symptoms identification using medical hypothesis models,” Cluster Comput., vol. 26, no. 4, pp. 2325–2337, 2023.
19. Y. A. Qadri, A. Nauman, Y. B. Zikria, A. V. Vasilakos, and S. W. Kim, “The future of healthcare Internet of Things: A survey of emerging technologies,” IEEE Commun. Surveys Tuts., vol. 22, no. 2, pp. 1121–1167, 2020, doi: 10.1109/COMST.2020.2973314.
20. Jamunadevi, P. Ragupathy, P. Sritha, S. Pandikumar, and S. Deepa, “Performance analysis of random forest classifier in extracting features from the EEG signal,” in Proc. Int. Conf. Adv. Comput. Technol. Appl. (ICACTA), Coimbatore, India, 2022, pp. 1–5, doi: 10.1109/ICACTA54488.2022.9753364
21. T. Margaret Mary, G. Ramanathan, A. Sangamithra, and K. Soumya, “Design and enactment of heart attack deduction using IoT measuring device,” J. Phys.: Conf. Ser., vol. 1427, no. 1, p. 012014, 2020, doi: 10.1088/1742-6596/1427/1/012014.
22. S. K. Zhou, H. Greenspan, C. Davatzikos, J. S. Duncan, B. van Ginneken, A. Madabhushi, J. L. Prince, D. Rueckert, and R. M. Summers, “A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises,” Proc. IEEE, vol. 109, no. 5, pp. 820–838, 2021, doi: 10.1109/JPROC.2021.3054390.
23. V. Pandimurugan, S. Ahmad, A. V. Prabu, M. K. I. Rahmani, H. A. M. Abdeljaber et al., “CNN-based deep learning model for early identification and categorization of melanoma skin cancer using medical imaging,” SN Comput. Sci., vol. 5, no. 7, p. 911, 2024, doi: 10.1007/s42979-024-03270-w.
24. L. S. Kumar, S. Hariharasitaraman, K. Narayanasamy, K. Thinakaran et al., “AlexNet approach for early stage Alzheimer’s disease detection from MRI brain images,” Mater. Today: Proc., vol. 51, pp. 58–65, 2022.
25. M. Tan and Q. V. Le, “EfficientNet: Rethinking model scaling for convolutional neural networks,” in Proc. 36th Int.Conf. Mach. Learn. (ICML), 2019, pp. 6105–6114.
26. L. Sathish Kumar, A. V. Prabu, V. Pandimurugan, S. Rajasoundaran et al., “A comparative experimental analysis and deep evaluation practices on human bone fracture detection using X-ray images,” Concurrency Comput.: Pract. Exper.,p. e7307, 2022.
27. J. C, P. V, A. P. D, P. S, and M. Manimaran, “Naive Bayes and SVM-based classification of myocardial tissue with echocardiography images for heart disease diagnosis,” in Proc. Int. Conf. Sustain. Comput. Netw. Appl. (ICSCNA), Theni, India, 2023, pp. 618–625, doi: 10.1109/ICSCNA58489.2023.10370212.
28. P. Kairouz et al., “Advances and open problems in federated learning,” Found. Trends Mach. Learn., vol. 14, no. 1–2, pp. 1–210, 2021, doi: 10.1561/2200000083.
29. S. Pandikumar, V. Pandimurugan, A. Rama Prasath, S. Karattil, S. C. B. Jaganathan, and P. Vigneshwaran, “A novel framework for differential privacy based federated continual learning for dynamic medical data analysis,” SN Comput. Sci., vol. 7, no. 6, p. 664, 2026, doi: 10.1007/s42979-026-05245-5.
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