Comparative Analysis Of Cardiovascular Disease Using Machine Learning Techniques

Abstract

Predicting cardiac disease is one of the utmost challenging challenges in the medical industry today. It is hard to pick out various cardiac diseases, because of several relevant health conditions such as Hypertension, Elevated blood pressure, hyperlipidemia, and irregular pulse rate with many factors. Heart disease is one of many illnesses that can be fatal, and it has received a lot of attention in medical studies. The detection of cardiac diseases is a more difficult task, but it can provide an accurate prognosis of the patient’s heart status to help with the purification step. Typically, the patient’s symptoms and warning signs are employed to determine the presence of cardiovascular disease. Cardiovascular disease seriousness is categorized using a variety of techniques,including Logistic Regression, Decision Tree Classifier, Random Forest, Svc, Naive Bayes, and KNN. The handling of cardiac diseaseis more difficult and we handle it with care, not doing may affect theheart or cause premature death. This study examines the performance of several models based on these algorithms and methodologies for the prediction of cardiac disease.