A hybrid extreme machine learning model for predicting heart disease

Abstract

Heart disease (HD), the leading cause of death for adults over 65, can affect anyone at any time. Additionally, modern lifestyles, poor diets, and other factors have led to an increased risk of HD among teenagers. One significant challenge is managing and analysing vast amounts of data, often surpassing terabytes, which is crucial for researching, diagnosing, and predicting cardiovascular diseases quickly. To enhance primary health care, especially in early and rapid diagnosis of heart attacks and to assist less experienced doctors in understanding clinical HD data, we propose a hybrid method called the "hybrid extreme machine learning model (HEMLM)". This technique combines the strengths of multi-layer perceptron (MLP), random layers, and logistic regression (LR). The model offers various feature patterns and multiple classification techniques. Compared to support vector machine (SVM), LR, and Naive Bayes (NB), the HEMLM algorithm demonstrates superior performance and efficiency. Testing results show identification accuracies of 94.91%, 94.77%, 92.42%, and 87.14% for data splitting ratios of 85:15, 80:20, 70:30, and 60:40, respectively.