Heart Disease Prediction using Machine Learning and Optimization based Algorithm

Abstract

Heart disease ranks among the most pressing health challenges worldwide, contributing to a significant number of deaths annually. The timely and precise forecasting of heart disease can greatly enhance patient results and lower healthcare expenses. In this initiative, we introduce a predictive framework for heart disease that employs machine learning methods combined with optimization algorithms to boost accuracy and efficiency. The model evaluates a range of clinical and physiological factors, including age, blood pressure, cholesterol levels, and heart rate, to assess the probability of heart disease. Machine learning techniques such as Logistic Regression, Random Forest, Support Vector Machine, and Neural Networks are employed to uncover intricate patterns within medical data, while optimization methods like Genetic Algorithm or Particle Swarm Optimization are used to refine model parameters and select features. This integrated strategy seeks to address the shortcomings of conventional prediction models and attain greater accuracy in diagnosing diseases. The suggested system acts as a crucial decision-support resource for healthcare providers, facilitating early identification, prompt intervention, and enhanced patient care results.