Author Login IJIRT Partner Network (IPN) Login

SCRUTINY AND ESTIMATE OF CARDIO VASCULAR AILMENT USING MACHINE LEARNING CLASSIFIERS

  • Unique Paper ID: 155908
  • Volume: 9
  • Issue: 3
  • PageNo: 179-182
  • 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.
Download article email to a friend

Cite This Article

  • ISSN: 2349-6002
  • Volume: 9
  • Issue: 3
  • PageNo: 179-182

SCRUTINY AND ESTIMATE OF CARDIO VASCULAR AILMENT USING MACHINE LEARNING CLASSIFIERS

Related Articles

Impact Factor
8.01 (Year 2024)
UGC Approved
Journal no 47859

Join Our IPN

IJIRT Partner Network

Submit your research paper and those of your network (friends, colleagues, or peers) through your IPN account, and receive 800 INR for each paper that gets published.

Join Now

Latest Publication

Archive

View More

Recent Conferences

NCSEM 2024

National Conference on Sustainable Engineering and Management - 2024 Last Date: 15th March 2024

Submit inquiry