Exploring Machine Learning Techniques for Accurate Heart Disease Prediction: An Evaluation of Model Efficacy and Performance Metrics
Keywords:
Introduction, Literature survey, Detection Technique, Logistic Regression, Support vector machine, K nearest neighbour, Decision Tree, Random ForestAbstract
Heart disease is still reason for death in today’s world, which has led to a great deal of study on reliable prediction methods. With an emphasis on the logistic regression model. In this paper, findings are done by using various machine learning algorithm. By presenting different methodologies, an overview of the body of research presents heart disease prediction. The study uses a dataset with a variety of variables on different algorithms like LR, SVM, KNN, DT, and RF. The model's efficacy is evaluated by looking at performance metrics. The machine learning algorithm Random Forest performs well in predicting heart disease, as seen by the experiment's 94.66 % accuracy rate.
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