Prediction Of Cardiovascular Disorders Using Machine Learning

Abstract

The paper delves into the application of various Machine Learning (ML) algorithms for the early identification and prediction of heart diseases. It examines the effectiveness of these algorithms in analyzing diverse datasets related to cardiac health, including medical history, lifestyle factors, and diagnostic tests results. By leveraging ML techniques such as Decision Trees, Support Vector Machines, and Neural Networks, researchers aim to develop robust predictive models capable of identifying individuals at risk of heart conditions with high accuracy. Additionally, the paper discusses the challenges associated with data collection, preprocessing, and model validation in the context of Heart Disease prediction, highlighting the need for further research and innovation in this critical area of healthcare. By scrutinizing datasets comprising patient data and clinical records, our objective is to construct resilient predictive models. Through meticulous evaluation and comparison of different algorithms, our study endeavors to identify the most efficient approaches for precise prediction. Ultimately, our research endeavors to facilitate proactive interventions and tailored healthcare interventions to mitigate the impact of heart diseases more efficiently.