Cardiovascular Disease Analysis and Diagnosis Using Machine Learning Models

Coronary disease, a predominant factor contributing to illness and death on a global scale, often exhibits diverse symptoms. For the timely intervention and enhanced patient results, early and precise prediction of coronary disease based on symptoms is crucial. New ways of predicting coronary disease have been enabled through developments in Machine learning (ML) with respect to symptom data. This research explores the use of machine learning approaches over a UCI dataset to predict the likelihood of having coronary disease. The dataset comes from UCI ML Repository with diverse patient health, lifestyle and medical history features. Methods include dataset analysis inclusive of data exploration, preprocessing, and feature engineering. Filling in missing values, encoding categorical variables, and normalizing numerical attributes are some of the ways that prepare the dataset for modeling purposes. Further, splitting the data into training and testing sets before using some ML algorithms to generate predictive models. These models are then evaluated using normal classification metrics like precision, accuracy, F score and recall. For every model hyper-parameter tuning is applied after which feature importance is investigated to pinpoint key elements concerning prediction of cases having coronary diseases respectively. The study contributes to the data-driven approaches for cardiovascular health and underscores the potential of machine learning in augmenting traditional diagnostic methods.