This paper presents a heart disease prediction system using machine learning techniques. The system uses logistic regression and decision tree algorithms to predict the likelihood of heart disease based on patient data such as blood pressure, cholesterol, fasting sugar, and age. The study emphasizes the importance of early detection of heart disease to improve patient outcomes and reduce mortality rates. The dataset used is from Kaggle, containing over 900 patients. The data was preprocessed to handle missing values, outliers, and inconsistencies. The dataset was split into training and testing sets, with 90% used for training and 10% for testing. The study found that logistic regression achieved an accuracy of 90.75%, which is higher than the accuracy of decision trees. The study also compares the performance of different machine learning algorithms and highlights the effectiveness of logistic regression in predicting heart disease. The system is designed to assist medical practitioners in making data-driven decisions and implementing targeted interventions. The study concludes that machine learning is a promising approach for predicting heart disease, but further research is needed to address challenges such as the need for large and diverse datasets, model interpretability, and ethical concerns regarding patient privacy. The study also emphasizes the importance of feature selection in improving the accuracy of heart disease prediction models.This paper presents a heart disease prediction system using machine learning techniques. The system uses logistic regression and decision tree algorithms to predict the likelihood of heart disease based on patient data such as blood pressure, cholesterol, fasting sugar, and age. The study emphasizes the importance of early detection of heart disease to improve patient outcomes and reduce mortality rates. The dataset used is from Kaggle, containing over 900 patients. The data was preprocessed to handle missing values, outliers, and inconsistencies. The dataset was split into training and testing sets, with 90% used for training and 10% for testing. The study found that logistic regression achieved an accuracy of 90.75%, which is higher than the accuracy of decision trees. The study also compares the performance of different machine learning algorithms and highlights the effectiveness of logistic regression in predicting heart disease. The system is designed to assist medical practitioners in making data-driven decisions and implementing targeted interventions. The study concludes that machine learning is a promising approach for predicting heart disease, but further research is needed to address challenges such as the need for large and diverse datasets, model interpretability, and ethical concerns regarding patient privacy. The study also emphasizes the importance of feature selection in improving the accuracy of heart disease prediction models.