Machine Learning and Big Data Analytics for Precision Cardiac RiskStratification and Heart Diseases

Abstract

The data explosion hasushered in a new era where insights are mined from vast data pools known as big data. Strategies for harnessing this data have emerged as critical decision-making tools across fields, employing various data analysis methods. Data mining techniques play an essential role in extracting meaningful patterns and insights. Thispaper focuses on the intersection of data mining and healthcare, particularly the critical concern of heart disease prediction.It presents a novel system that estimates heart attack risk, combining data mining with machine learning. Employing classification, the system stratifies data intotwo classes: heart disease presence or absence. Two powerful algorithms,decision tree classification and Naïve Bayes classification, enhance accuracy in predicting heart disease risk, achieving up to 91% and 87% accuracy, respectively. This review paper comprehensively analyzes the system's architecture, methodologies, and outcomes in healthcare, emphasizing data mining and machine learning's potential in medicine. Subsequent sections delve into methodology, results, and implications,providing a holistic view of this innovativeapproach.