A Comprehensive Review of Machine Learning and Explainable AI Techniques for Disease Prediction Systems

Year : 2026 | Volume : 04 | Issue : 01 | Page : 20 28
    By

    Swarupa Arjya,

  • Smruti Ranjan Tripathy,

  • Ritish Bhadra,

  • Bivash Ranjan Swain,

  1. Assistant Professor, Department of Computer Science and Engineering, GIET Ghangapatna, Bhubaneswar, Odisha, India
  2. Student, Department of Computer Application, Ghangapatna, Bhubaneswar, Odisha, India
  3. Student, Department of Computer Application, Ghangapatna, Bhubaneswar, Odisha, India
  4. Student, Department of Computer Application, Ghangapatna, Bhubaneswar, Odisha, India

Abstract

Large amounts of diverse medical data have been produced because of the quick development of digital healthcare systems, offering substantial chances to use machine learning methods for clinical decision support and illness prediction. By identifying intricate patterns in clinical data, machine learning-based models have shown great promise in early disease detection, risk assessment, and personalised healthcare. However, issues with transparency, interpretability, and reliability have been brought up by the growing complexity of sophisticated models, like ensemble learners and deep neural networks, especially in safety-critical healthcare applications. Explainable AI techniques have become a crucial part of contemporary intelligent healthcare systems to overcome these issues. With a focus on computational and algorithmic viewpoints pertinent to computer science and engineering researchers, this paper provides a thorough overview of machine learning and explainable AI techniques used for disease prediction. Data sources, preprocessing approaches, feature selection techniques, deep learning architectures, conventional and sophisticated machine learning models, class imbalance handling strategies, and performance evaluation criteria are all reviewed in this work. It also offers a thorough explanation of explainable AI methods and how they might improve clinical trust and model transparency. To facilitate the creation of scalable, comprehensible, and reliable healthcare prediction systems, the main obstacles, constraints, and future research areas are finally described.

Keywords: Machine learning, explainable artificial intelligence, disease prediction, deep learning, digital healthcare

[This article belongs to International Journal of Biomedical Innovations and Engineering ]

How to cite this article:
Swarupa Arjya, Smruti Ranjan Tripathy, Ritish Bhadra, Bivash Ranjan Swain. A Comprehensive Review of Machine Learning and Explainable AI Techniques for Disease Prediction Systems. International Journal of Biomedical Innovations and Engineering. 2026; 04(01):20-28.
How to cite this URL:
Swarupa Arjya, Smruti Ranjan Tripathy, Ritish Bhadra, Bivash Ranjan Swain. A Comprehensive Review of Machine Learning and Explainable AI Techniques for Disease Prediction Systems. International Journal of Biomedical Innovations and Engineering. 2026; 04(01):20-28. Available from: https://journals.stmjournals.com/ijbie/article=2026/view=247030


References

  1. Roth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM, et al. Global burden of cardiovascular diseases and risk factors, 1990–2019: update from the GBD 2019 study. Lancet. 2020;396(10258):1223–1249.
  2. Hastie T, Tibshirani R, Friedman J. The elements of statistical learning: data mining, inference, and prediction. 2nd ed. New York (NY): Springer; 2009. 745 p. ISBN: 978-0-387-84857-0.
  3. Goodfellow I, Bengio Y, Courville A. Deep learning. Cambridge (MA): MIT Press; 2016. 775 p. ISBN: 978-0-262-03561-3.
  4. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. A guide to deep learning in healthcare. Nat Med. 2019;25(1):24–29.
  5. Kourou K, Exarchos TP, Exarchos KP, Karamouzis MV, Fotiadis DI. Machine learning applications in cancer prognosis and prediction. Comput Struct Biotechnol J. 2015;13:8–17.
  6. Shickel B, Tighe PJ, Bihorac A, Rashidi P. Deep EHR: A survey of recent advances in deep learning techniques for electronic health record analysis. IEEE J Biomed Health Inform. 2018;22(5):1589–1604.
  7. Rudin C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat Mach Intell. 2019;1(5):206–215.
  8. Ribeiro MT, Singh S, Guestrin C. Why should I trust you? Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’16); 2016 Aug 13–17; San Francisco, CA, USA. New York (NY): Association for Computing Machinery (ACM); 2016. p. 1135–1144. doi:10.1145/2939672.2939778.
  9. Lundberg SM, Lee SI. A unified approach to interpreting model predictions. In: Guyon I, Luxburg UV, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R, editors. Advances in Neural Information Processing Systems 30 (NeurIPS 2017); 2017 Dec 4–9; Long Beach, CA, USA. Red Hook (NY): Curran Associates, Inc.; 2017. p. 4765–4774.
  10. Chen T, Guestrin C. XGBoost: A scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’16); 2016 Aug 13–17; San Francisco, CA, USA. New York (NY): Association for Computing Machinery (ACM); 2016. p. 785–794. doi:10.1145/2939672.2939785.
  11. Breiman L. Random forests. Mach Learn. 2001;45(1):5–32. doi: 1023/A:1010933404324.
  12. Cortes C, Vapnik V. Support-vector networks. Mach Learn. 1995;20(3):273–297.
  13. Fawcett T. An introduction to ROC analysis. Pattern Recognit Lett. 2006;27(8):861–874.
  14. Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE: Synthetic minority over-sampling technique. J Artif Intell Res. 2002;16:321–357.
  15. Hinton GE, Deng L, Yu D, Dahl GE, Mohamed AR, Jaitly N, et al. Deep neural networks for acoustic modeling in speech recognition. IEEE Signal Process Mag. 2012;29(6):82–97.
  16. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436–444.
  17. Rajkomar A, Oren E, Chen K, Dai AM, Hajaj N, Hardt M, et al. doi:10.1038/s41746-018-0029-1.
  18. Topol E. Deep medicine: how artificial intelligence can make healthcare human again. New York (NY): Basic Books; 2019. 400 p. ISBN: 978-1-5416-4675-0.
  19. Han J, Kamber M, Pei J. Data mining: concepts and techniques. 3rd ed. Waltham (MA): Morgan Kaufmann; 2011. 703 p. ISBN: 978-0-12-381479-1.
  20. Samek W, Wiegand T, Müller KR. Explainable artificial intelligence: understanding, visualizing and interpreting deep learning models. ITU J ICT Discov. 2017;1(1):1–10.
  21. Obermeyer Z, Powers B, Vogeli C, Mullainathan S. Dissecting racial bias in an algorithm used to manage the health of populations. Science. 2019;366(6464):447–453. doi: 1126/science.aax2342.
  22. Arrieta AB, Díaz-Rodríguez N, Del Ser J, Bennetot A, Tabik S, Barbado A, et al. Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges. Inf Fusion. 2020;58:82–115.
  23. Abdar M, Salama GI, Hussain S, Khan AR, Khosravi A, Acharya UR, et al. A review of uncertainty quantification in deep learning. Inf Fusion. 2021;76:243–297.
  24. Chen H, Chiang RH, Storey VC. Machine learning for large-scale wearable sensor data in healthcare. IEEE Access. 2020;8:18172–18188.
  25. Kim JK, Kang HJ. Heart disease prediction using ensemble methods. IEEE Access. 2017;5:1458–1468.
  26. Rajpurkar P, Irvin J, Zhu K, Yang B, Mehta H, Duan T, Ding D, Bagul A, Langlotz C, Shpanskaya K, Lungren MP, Ng AY. CheXNet: radiologist-level pneumonia detection on chest X-rays with deep learning [Preprint]. arXiv:1711.05225; 2017 Nov 14. Available from: arXiv CheXNet Paper. doi:10.48550/arXiv.1711.05225.
  27. Miotto R, Wang F, Wang S, Jiang X, Dudley JT. Deep learning for healthcare: Review, opportunities and challenges. Brief Bioinform. 2018;19(6):1236–1246.
  28. Beam AL, Kohane IS. Big data and machine learning in health care. JAMA. 2018;319(13):1317–1318.
  29. Pesapane F, Volonté C, Codari M, Sardanelli F. Artificial intelligence as a medical device in radiology. Eur Radiol. 2018;28(12):531–538.
  30. Chen M, Hao Y, Hwang K, Wang L, Wang L. Disease prediction by machine learning over big healthcare data. IEEE Access. 2017;5:8869–8879.
  31. Tjoa E, Guan C. A survey on explainable artificial intelligence (XAI). IEEE Trans Neural Netw Learn Syst. 2021;32(11):4793–4813.
  32. Dash S, Shakyawar SK, Sharma M, Kaushik S. Explainable AI for healthcare. IEEE Internet Comput. 2021;25(1):21–29.
  33. Yang Q, Liu Y, Chen T, Tong Y. Federated machine learning in healthcare. IEEE Intell Syst. 2019;34(4):54–64.
  34. Li Q, Wen Z, Wu Z, Hu S, Wang N, He B, et al. A survey on federated learning systems. arXiv [Preprint]. 2019. Available from: https://arxiv.org/abs/1902.04885
  35. Sun J, Chen M, Li Y, Zhang Y. Using deep learning for healthcare. IEEE Eng Med Biol Mag. 2019;38(2):12–19.
  36. Raghupathi W, Raghupathi V. Big data analytics in healthcare: promise and potential. Health Inf Sci Syst. 2014;2:3. doi:10.1186/2047-2501-2-3.
  37. Ahmad MA, Eckert C, Teredesai A. Interpretable machine learning in healthcare. ACM Comput Surv. 2021;54(5):1–32.
  38. Vellido A. The importance of interpretability in healthcare machine learning. Neural Comput Appl. 2020;32:18069–18083.
  39. Kumar A, Gupta PK, Srivastava A. Explainable AI for healthcare. Sensors (Basel). 2020;20(21):6250.
  40. He J, Baxter SL, Xu J, Xu J, Zhou X, Zhang K. AI technologies in medicine. Nat Med. 2019;25(1):30–36.
  41. Min S, Lee B, Yoon S. Deep learning in bioinformatics. Brief Bioinform. 2017;18(5):851–869.
  42. Aha DW. Lazy learning. Artif Intell Rev. 1997;11(1–5):7–10.
  43. Zhang J, Xing L, Yan K, Yang L. Machine learning for clinical decision support systems. IEEE Rev Biomed Eng. 2022;15:3–20.
  44. Holzinger A, Carrington A, Müller H. Measuring explanation quality. Wiley Interdiscip Rev Data Min Knowl Discov. 2020;10(5):e1312.
  45. Caruana R, Lou Y, Gehrke J, Koch P, Sturm M, Elhadad N. Intelligible models for healthcare. In: Proceedings of KDD. 2015. p.1721–1730.
  46. Holzinger A, Langs G, Denk H, Zatloukal K, Müller H. Causability and explainability of artificial intelligence in medicine. Wiley Interdiscip Rev Data Min Knowl Discov. 2019;9(4):e1312. doi:10.1002/widm.1312.
  47. Samek W, Montavon G, Vedaldi A, Hansen LK, Müller KR, editors. Explainable AI: interpreting, explaining and visualizing deep learning. Cham (Switzerland): Springer; 2019. 439 p. (Lecture Notes in Computer Science; vol. 11700). doi:10.1007/978-3-030-28954-6.
  48. Voigt P, von dem Bussche A. The EU General Data Protection Regulation (GDPR): a practical guide. 1st ed. Cham (Switzerland): Springer International Publishing; 2017. ix, 383 p. doi:10.1007/978-3-319-57959-7.
  49. Belle A, Thiagarajan R, Soroushmehr SMR, Navidi F, Beard DA, Najarian K. Big data analytics in healthcare. Biomed Res Int. 2015;2015:370194.
  50. Alizadehsani R, Roshanzamir M, Hussain S, Khosravi A, Koohestani A, Zangooei MH, Abdar M, et al. Handling of uncertainty in medical data using machine learning and probability theory techniques: a review of 30 years (1991–2020). Ann Oper Res. 2021;1–42. doi:10.1007/s10479-021-04006-2.
Regular Issue Subscription Review Article
Volume 04
Issue 01
Received 30/01/2026
Accepted 02/02/2026
Published 09/06/2026
Publication Time 130 Days
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