Heart diseases prediction

Predicting heart diseases using AI and machine learning is crucial for early diagnosis and prevention. These models rely on health data such as age, blood pressure, cholesterol levels, and lifestyle habits to predict the risk of heart disease. After collecting and cleaning the data, key features are selected, and algorithms like logistic regression, decision trees, or neural networks are used to build predictive models. The accuracy of these models is evaluated on test data, with the goal of providing reliable predictions. Challenges include ensuring high-quality data and balancing sensitivity to avoid false positives.