A Comprehensive Review of Machine Learning and Explainable AI Techniques for Disease Prediction Systems - Swarupa Arjya; Smruti Ranjan Tripathy; Ritish Bhadra; Bivash Ranjan Swain (International Journal of Biomedical Innovations and Engineering, 2026)
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: Deep Learning; Machine Learning; digital healthcare; disease prediction; explainable artificial intelligence