Machine Learning Models for Disease Prediction In Healthcare

Abstract

Machine learning (ML) has emerged as a game-changing tool for disease prediction in healthcare, with the ability to handle massive and complicated medical datasets obtained from electronic health records, wearable devices, and patient registries. This review systematically investigates the use of machine learning models such as classical algorithms, ensemble learning, and deep learning for early diseases diagnosis and prognosis in a variety of ailments, including cardiovascular disease, cancer, and neurological disorders. Recent research show that ML adoption improves prediction accuracy, patient classification, and treatment methods. Despite these advancements, issues remain in data quality, privacy, model explainability, and generalizability across varied populations. Integrating ML into clinical processes necessitates rigorous regulatory monitoring to assure model safety and transparency. Furthermore, ethical concerns about health data usage and equality remain crucial. By examining current studies and major developments, this study highlights ML's potential to change disease prediction, promote individualized treatment, and enable proactive interventions in healthcare settings. Future research should prioritize improving the robustness and interpretability of ML models, establishing regulatory frameworks, and addressing ethical concerns to provide fair benefits for all patients.