A Comparative Analysis of the Effectiveness of Multiple Models for Predicting Heart Failure using Data Mining

Abstract

One of the most fatal and well-known diseases worldwide, heart disease claims the lives of many people every year. In order to preserve lives, early detection regarding such disease is essential. One of the quickest, practical, and affordable methods of disease detection is Data Mining DM, an artificial intelligence AI technology. Human life is saved by healthcare services through prompt and efficient decision-making. For forecasting, decision-making, and disease prediction, DM technologies are essential. This research predicts heart disease using DM algorithms. There are 14 attributes in Cleveland dataset, including blood fat, blood pressure, gender, and age. The probability regarding patients developing heart disease in the future can be forecasted by analyzing such parameters. For classifying if heart disease is present or absent, two classification algorithms are used: Logistic Regression LR and K-Nearest Neighbor KNN. The precision, accuracy, f-score, and recall of the suggested model are evaluated. The outcomes of suggested model were tested using the heart disease dataset. Without preprocessing the dataset's variation values, the LR and KNN algorithms achieved the highest accuracy (61% and 71%, respectively). The algorithms (LR and KNN) preprocessed the dataset's variation values to get the highest accuracy (90% and 93%). In order to improve data driven medical decision-making, the presented research demonstrates how well DM algorithms work to increase heart disease prediction accuracy.