Heart disease detection system

Abstract

This paper presents a comprehensive exploration into the utilization of machine learning (ML) techniques to revolutionize medical diagnostics, with a specific focus on enhancing the detection of heart disease. Recognizing the imperative need for early diagnosis to address the global prevalence of heart disease, this study delves into the development and application of advanced ML principles. The paper aims to construct a robust ML model capable of analyzing diverse patient data sets, including electronic health records and genetic information, to discern intricate patterns and correlations imperceptible to human clinicians. By leveraging a comprehensive dataset encompassing various patient profiles, the ML model is poised to significantly enhance the precision, speed, and efficiency of heart disease detection. The findings of this paper hold promise for fostering more effective intervention strategies and improving patient care outcomes in the realm of cardiovascular health.