AN EMPIRICAL STUDY AND ANALYSIS OF HEART DISEASE PREDICTION

An empirical study and analysis of heart disease prediction involves using data analysis techniques to identify patterns and risk factors associated with cardiovascular disease. This approach utilizes machine learning algorithms to classify patients based on their likelihood of developing heart disease.
The study involves collecting data on risk factors such as age, gender, family history, blood pressure, cholesterol levels, smoking, and diabetes. Feature selection techniques are used to identify the most important risk factors, and a classification model is trained using these factors. The accuracy of the model is evaluated using metrics such as sensitivity, specificity, and AUC.
This empirical study and analysis has several advantages, including the ability to identify new risk factors associated with heart disease, improved accuracy in predicting cardiovascular risk, and the potential to develop more personalized prevention and treatment strategies. This approach has the potential to improve medical decision-making and reduce the burden of heart disease on individuals and society.