Abstract

Recent advancements in artificial intelligence (AI) and machine learning (ML) have transformed the healthcare industry, enabling data-driven insights for improved diagnosis, treatment, and patient management. This research explores the implementation of machine learning algorithms for early disease detection using clinical and demographic data. We evaluate the performance of various supervised learning models—such as logistic regression, random forest, and deep neural networks—on a curated dataset comprising patient health records. The study demonstrates that the proposed hybrid model achieves higher accuracy and recall compared to traditional diagnostic methods. Our findings suggest that integrating AI-driven predictive systems within healthcare frameworks can enhance preventive care, reduce diagnosis time, and support medical decision-making. The paper concludes with recommendations for real-world deployment and outlines ethical considerations in AI-based healthcare systems.