Paper Title
TOWARDS PRECISION MEDICINE: MACHINE LEARNING-ENHANCED DATA ANALYSIS FOR HEART FAILURE PREDICTION

Abstract
A comprehensive study is presented, focused on predicting heart disease using patient health data, with the aim of supporting clinical decision-making and improving the accuracy of medical analysis. The research involved meticulous analysis of a large dataset comprising numerous instances and diverse attributes, including age, cholesterol levels, blood pres- sure, and other critical health indicators. Rigorous exploratory data analysis and sophisticated feature engineering techniques were applied to preprocess the data, ensuring its suitability for machine learning model training. The performance of various machine learning algorithms was evaluated to determine their efficacy in predicting heart disease. The models were rigorously assessed using a range of metrics, including accuracy, precision, recall, and F1 score, to provide a holistic view of their strengths and limitations. The findings demonstrate that machine learning models can effectively identify high-risk patients based on routine health metrics, providing a valuable tool for clinicians to support early intervention and personalized treatment planning. This capability is shown to have significant implications for improving the reliability of heart disease prediction models, optimizing resource allocation, and enhancing healthcare efficiency. Keywords - Recall, F1-Score, Medical Analysis, Data Analysis, Machine Learning Models