Heart Disease Prediction Using Machine Learning: A Data-Driven Approach
Abboskhujaev Akhrorkhuja, Danish Ather, Rahul Chauhan, Kireet Joshi, Kulbir Singh, Naina Chaudhary
Abstract
Heart sickness remains a main purpose of mortality worldwide, accounting for a significant percentage of worldwide deaths. Early detection and correct diagnosis are important in reducing its impact and enhancing affected person effects. This look at proposes a machine learning-based totally approach for heart sickness prediction, utilising a dataset of scientific fitness parameters along with age, gender, levels of cholesterol, and blood pressure. The version is evolved the usage of a mixture of algorithms, inclusive of logistic regression, random woodland, and aid vector machines (SVM), to are expecting the probability of heart sickness. Feature choice strategies are implemented to discover the most crucial parameters influencing heart disorder danger. The dataset is break up into training and testing units, and the models are evaluated primarily based on accuracy, precision, bear in mind, and F1-score. Our experimental outcomes display that the random forest version performed the very best performance with an accuracy of 85%, outperforming different fashions. This technique demonstrates the ability of machine gaining knowledge of in helping early prognosis and customized treatment making plans for sufferers susceptible to coronary heart sickness. The proposed method can be incorporated into healthcare structures to enhance predictive skills and facilitate proactive healthcare interventions. Future work will discover the inclusion of more various datasets to enhance the model's generalizability.