Cardiovascular Disease Prediction Using Machine Learning: A comprehensive Study

cardiovascular diseases (CVDs) represent a significant global health challenge and need appropriate prediction models to make an early diagnosis and treatment. The given study develops a machine learning based prediction model using data science techniques to identify the people who are exposed to CVDs risk. Different ML algorithms used for the prediction of a model are logistic regression, random forests, gradient boosting, and neural networks. The model was trained using clinical datasets incorporating demographic information, medical history, and lifestyle factors. Performance metrics include accuracy, precision, recall and F1-score for selecting the most appropriate algorithm. The results indicate that when ML is combined with appropriate data processing techniques, there is a considerable improvement in prediction accuracy, and this might find applications in preventive healthcare.