Machine Learning for Early Disease Detection: A Systematic Review and Future Directions

Abstract

Machine learning (ML) has emerged as a transformative paradigm in healthcare, particularly in early disease detection where timely diagnosis significantly improves patient outcomes. This systematic review examines 87 peer-reviewed studies published between 2018 and 2024, analyzing the application of supervised, unsupervised, and deep learning algorithms across seven major disease categories including cardiovascular disorders, diabetes, cancer, neurological conditions, respiratory diseases, infectious diseases, and rare genetic disorders. We evaluate model performance metrics, dataset characteristics, feature engineering strategies, and clinical validation approaches. Our findings reveal that deep convolutional neural networks consistently outperform traditional classifiers in image-based diagnostics achieving average AUC of 0.94, while ensemble methods demonstrate superior performance in tabular clinical data with F1-scores exceeding 0.89. Despite promising results, significant challenges persist including limited dataset diversity, lack of clinical interpretability, and regulatory barriers. This review provides a structured roadmap for bridging the gap between ML research and clinical deployment, with particular emphasis on explainable AI frameworks and federated learning as emerging solutions to privacy-preserving medical AI.