G.R. Rao, Shourya Khujneri, Atul Kumar Tomar, Rudraksh Sharma
Machine Learning, Decision Tree Classifier, Random Forest Classifier, SVM, Logistic Regression, k-nearest neighbor
The contents of this study primarily focus on various data mining approaches that are useful in predicting heart disease using various data mining technologies that are available. The brain, kidneys, and other areas of the human body will suffer if the heart does not operate properly. Heart disease is a condition that impairs the heart's ability to operate. In today's world, heart disease is the leading cause of mortality. This study gathered data from a variety of sources and divided it into two parts: 80 percent for the training dataset and 20% for the test dataset. Different classifier methods were used to improve accuracy, which was then summarised. Random Forest Classifier, Decision Tree Classifier, Support Vector Machine, k-nearest neighbour, Logistic Regression, and Naive Bayes are the methods in question. SVM, Logistic Regression, and KNN all performed as well as or better than other methods. This research offers a development in which fundamental prefixes such as sex, glucose, blood pressure, heart rate, and others are used to determine which factors are prone to heart disease. The paper's next aim is to conduct real-life tests using various equipment and clinical trials.
Article Details
Unique Paper ID: 155329

Publication Volume & Issue: Volume 9, Issue 1

Page(s): 606 - 611
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