Comparative Analysis of Single Classifier Models against Aggregated Fusion Models for Heart Disease Prediction

Abstract

The current focus of research is on using machine learning (ML) algorithms to predict heart disease. Using the UC Irvine (UCI) Cleveland Heart Disease dataset, this study investigates the effectiveness of various types of classifiers, including K-Nearest Neighbours (KNN), AdaBoost, Gaussian Naïve Bayes (GNB), support vector machines (SVM), multilayer perceptron (MLP) and random forests. The objective of this study is to assess the precision and speed of each classifier and gauge their effectiveness by utilizing measures like accuracy and F1 score for comparison. The study also looks into the potential benefits of fusion methods for improving the accuracy of heart disease prediction. The study concludes that combining various models could lead to improving the metrics. Our study contributes to the ongoing research on heart disease prediction using ML algorithms. The findings of our study can be used to develop more precise models for predicting heart disease, which can aid in improving clinical decision-making for heart disease prevention and treatment.