Heart Disease Prediction Using various Machine Learning Algorithms

  • Swathi. A , E. Lakshmi Neeharika
Keywords: Data Collection, Smart Health Disease Prediction, Classification and Prediction.

Abstract

Cardiac arrest is a very serious and can happen at any time. If you wake up with throbbing, acute complications in your chest, it's a sign that you're having a cardiac arrest. Cardiac arrest has become increasingly popular in recent years. We will create a mechanism for forecasting heart attacks in this research. We obtain the datasets from either the Uci machine learning (ML) repository or Kaggle, and then divide it into two pieces, namely the Training dataset and the Testing dataset. Eighty percent of the data are used for training, while the remaining twenty percent are used for evaluation. After that, we'll use a variety of ML techniques for the analysis of information we've collected. The efficiency of several strategies is then compared, and the ML methodology with high precision is ultimately recommended.

Published
2021-09-01
How to Cite
Swathi. A , E. Lakshmi Neeharika. (2021). Heart Disease Prediction Using various Machine Learning Algorithms. Design Engineering, 10640-10646. Retrieved from http://www.thedesignengineering.com/index.php/DE/article/view/3938
Section
Articles