Analysis of traditional machine learning approaches on heart attacks prediction.

Considering the persistent challenge of early heart attack detection in patients, despite significant advancements in medical systems, this research project is motivated by the imperative need to develop effective predictive machine learning models. The central problem addressed here in is the identification of individuals at risk of experiencing a heart attack. In response to this problem, two distinct models have been devised and meticulously evaluated, namely decision trees and logistic regression, each designed to fulfil the primary objective of this research. Through a rigorous analysis and thorough evaluation of the results, we have scrutinised the performance of these models. The comparison between decision trees and logistic regression provides valuable insights into their efficacy in predicting heart attacks. The culmination of this endeavor not only contributes to the growing body of knowledge in heart attack prediction and provides healthcare professionals with powerful tools for early diagnosis, potentially saving lives and improving patient outcomes. [ABSTRACT FROM AUTHOR]