Heart disease remains a major cause of mortality worldwide and is an early and accurate prediction of its critical importance for preventive care.Traditional diagnostic methods are often time-consuming, expensive and clinically specialized knowledge. This study examines the use of machinelearning techniques for the development of predictive models to detect heart attacks using patient health data. Research uses Python-based librariessuch as Scikit-Learn, Pandas, and Tensorflow to prepare your data, select important features, and select classification models. Compare diHerentalgorithms for machine learning, including logistics regression, random forests, support vector machines (SVMs), and neural networks, todetermine the mosteHective approach. Data records are from the UCI repository for machine learning and contain important health indicators suchascholesterol, blood pressure, and ECG measurements. Evaluate the model output using evaluation metrics such as accuracy, accuracy, recall, and F1score. Experimental results show that the random forest classifier reaches the highest accuracy (85%), making it a promising tool for predicting heartattacks. This study uncovers the potential for early diagnosis and preventive measures, and ultimately the possibility of AI-controlled health solutionsin improving mortality and patient outcomes. Future work will include improving deep learning models and real-time integration of wearable healthdata to improve prediction accuracy.
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