Paper Title
Heart Disease Prediction Using Machine Learning

Abstract
Heart disease stands as a formidable global threat to human health, persisting as a leading cause of mortality. Our study presents a machine learning-based predictive model for heart disease utilizing a diverse dataset of demographic, clinical, and lifestyle factors. Through the application of various algorithms, including logistic regression, random forest, and decision trees, we accurately predict the likelihood of heart disease occurrence. Comparative analysis highlights the strengths and limitations of different algorithms. The developed models offer valuable insights for early diagnosis and personalized intervention strategies. This research contributes to the advancement of predictive analytics in healthcare, paving the way for more effective and targeted disease management approaches. Keywords - Heart Disease, Machine Learning Algorithms, Logistic Regression, Decision Trees