Heart Arrhythmia Disease Prediction Using Logistic Regression Algorithms

Heart disease arrhythmia is a significant medical concern affecting millions of individuals worldwide. Early and accurate prediction of arrhythmias can play a crucial role in improving patient outcomes and reducing the burden on healthcare systems. The primary objective of this research is to compare the performance of these machine learning models in predicting heart disease arrhythmias. The models are trained and evaluated using standard accuracy metrics. The achieved accuracy rates for each model are as follows Naïve Bayes - 80%, Support Vector Machine (SVM) - 81%, Decision Tree - 83%, and Logistic Regression - 87%. The results suggest that Logistic Regression performs the best among the tested models, achieving an accuracy rate of 87%. This indicates that the selected features and the algorithm’s underlying assumptions align well with the characteristics of heart disease arrhythmia data. While all models show promising accuracy rates, Logistic Regression stands out as a robust choice for arrhythmia prediction.