The paper "Heart Disease Prediction System Using Machine Learning" by Dr. Umesh Akare, Prof. Umme Ayeman Gani, Anushri Bhongade, Dhanashree Mure, Madhulika Chatterjee, and Vanzuli Ramteke explores the use of machine learning techniques to predict heart disease. The authors highlight the importance of early detection of cardiovascular diseases and the potential of machine learning to provide valuable insights for personalized diagnosis and treatment. They employ two primary machine learning algorithms—Logistic Regression and Decision Trees—to predict heart disease based on patient data such as blood pressure, cholesterol, age, and gender. The study involves data collection from a web-based repository (Kaggle), data preprocessing to handle missing values and outliers, feature selection to identify relevant attributes, and data splitting into training and testing sets. The model is trained using the training dataset and evaluated using the testing dataset. The results show that the combination of Logistic Regression and Decision Trees achieves an accuracy of 90.75% with a 90:10 training-to-testing split. The authors conclude that while the model performs well with a limited dataset, larger and more diverse datasets are needed for further validation and improvement. They also emphasize the importance of continuous model validation and ethical considerations in patient data privacy. Keywords: Machine learning, supervised learning, logistic regression, decision tree, Python programming.The paper "Heart Disease Prediction System Using Machine Learning" by Dr. Umesh Akare, Prof. Umme Ayeman Gani, Anushri Bhongade, Dhanashree Mure, Madhulika Chatterjee, and Vanzuli Ramteke explores the use of machine learning techniques to predict heart disease. The authors highlight the importance of early detection of cardiovascular diseases and the potential of machine learning to provide valuable insights for personalized diagnosis and treatment. They employ two primary machine learning algorithms—Logistic Regression and Decision Trees—to predict heart disease based on patient data such as blood pressure, cholesterol, age, and gender. The study involves data collection from a web-based repository (Kaggle), data preprocessing to handle missing values and outliers, feature selection to identify relevant attributes, and data splitting into training and testing sets. The model is trained using the training dataset and evaluated using the testing dataset. The results show that the combination of Logistic Regression and Decision Trees achieves an accuracy of 90.75% with a 90:10 training-to-testing split. The authors conclude that while the model performs well with a limited dataset, larger and more diverse datasets are needed for further validation and improvement. They also emphasize the importance of continuous model validation and ethical considerations in patient data privacy. Keywords: Machine learning, supervised learning, logistic regression, decision tree, Python programming.