SCRUTINY AND ESTIMATE OF CARDIO VASCULAR AILMENT USING MACHINE LEARNING CLASSIFIERS

SCRUTINY AND ESTIMATE OF CARDIO VASCULAR AILMENT USING MACHINE LEARNING CLASSIFIERS
Author(s):
K.Pooja Sri, D.Thanmaye Varma, G.Jyoshna, V.Anantha Krishna
Keywords:
Random Forest, Machine Learning classifier, Decision tree.
Abstract
For the most part, Cardio Vascular Disease (CVD) refers to disorders that include narrowed or blocked veins, which can lead to a heart attack, chest pain (angina), or stroke. The condition is predicted by the machine learning classifier based on the state of the patient's side effect. The purpose of this research is to examine the presentation of Machine Learning Tree Classifiers in the prediction of Cardiovascular Disease (CVD). Random Forest, Decision Tree, Logistic Regression, Support vector machine (SVM), and K-nearest neighbours (KNN) were used to break down machine learning tree classifiers based on their precision and AUC ROC scores. The Random forest, Machine learning classifier achieved a greater precision of 85 percent, ROC AUC score of 0.8675, and execution time in this study of predicting Cardiovascular Disease.
Article Details
Unique Paper ID: 155908 Publication Volume & Issue: Volume 9, Issue 3 Page(s): 179 - 182
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