Comprehensive machine learning approaches for disease prediction current applications recent advances and future prospects
TL;DR
The reviewed studies demonstrated that Deep learning approaches, particularly convolutional neural networks, transformer-based models, and multimodal frameworks, showed improved predictive accuracy when large and diverse datasets were available.
Abstract
Machine learning (ML) and deep learning (DL) models are increasingly applied for disease prediction, diagnosis, and risk stratification across a wide range of medical conditions. This review explores current breakthroughs in ML and DL-based disease prediction, with a particular focus on cancer, diabetes, and cardiovascular diseases (CVDs), Alzheimer’s, Parkinson’s disease (PD) and chronic kidney diseases (CKD). A narrative review with structured literature selection was conducted using PubMed and Google Scholar, focusing on studies from 2021 to 2026, articles were included based on their relevance with machine and deep learning in disease prediction. The reviewed studies demonstrated that Deep learning approaches, particularly convolutional neural networks, transformer-based models, and multimodal frameworks, showed improved predictive accuracy when large and diverse datasets were available. Explainable AI techniques enhanced model transparency, while federated learning emerged as promising solution for privacy-preserving model development. However, challenges related to data heterogeneity, limited external validation, class imbalance, model interpretability, and clinical integration remain significant barriers to widespread implementation. Overall, this review highlights the significant potential of ML-DL driven disease prediction while emphasizing the need for robust validation, transparency, clinical integration to ensure safe and effective-real world applications.