Publisher's Synopsis
A reliable and accurate heart disease prediction system uses machine learning algorithms to predict the likelihood of heart disease based on a set of risk factors. This system utilizes decision tree, Naive Bayes, random forest, and support vector machine algorithms to analyze patient data and identify patterns that are indicative of cardiovascular disease.
Feature selection techniques are used to identify the most important risk factors, which may include age, gender, family history, blood pressure, cholesterol levels, smoking, and diabetes. The accuracy of the model is evaluated using metrics such as sensitivity, specificity, and AUC.
This system has several advantages, including improved accuracy in predicting heart disease risk, the ability to identify patients at high risk for cardiovascular disease, and the potential to integrate data from electronic health records and other sources. This approach has the potential to improve medical decision-making, provide more personalized care for patients, and reduce the burden of heart disease on individuals and society.
