Early Disease Detection and Prediction using Machine Learning

Abstract

Early disease detection plays a critical role in improving treatment outcomes and reducing mortality rates. With the exponential growth in healthcare data and advances in computational power, Machine Learning (ML) has emerged as a transformative tool in medical diagnostics. The integration of ML techniques into healthcare has revolutionized disease diagnosis and management. Early detection is vital in reducing mortality and ensuring timely treatment. This research paper explores various ML techniques/algorithms and their applications in the early detection and prediction of diseases, including cancer, diabetes, and cardiovascular conditions. The effectiveness of supervised and unsupervised models, evaluate real-world case studies, and highlight the challenges and ethical considerations involved. The findings demonstrate that ML can significantly enhance clinical decision-making when models are designed with accuracy, transparency, and fairness in mind.