Heart Stroke Prediction Using Machine Learning

Heart Stroke Prediction investigates the application of machine learning techniques for predicting heart stroke risk based on patient data. The study focuses on utilizing Support Vector Machine to develop a predictive model for heart stroke risk assessment. The dataset comprises clinical and demographic information from patients who have either experienced a stroke or are at risk of one. The data is preprocessed to ensure accuracy and consistency before training the models. Model performance is evaluated using metrics such as accuracy, precision, recall, F1-score, and a confusion matrix to assess the ability of each model to distinguish between high and low-risk individuals. The research demonstrates that machine learning algorithms can significantly enhance heart stroke prediction by identifying key risk factors and patterns within the dataset.