Heart Disease Prediction System using hybrid model of Multi-layer perception and XGBoost algorithms

Alnahrain university, Baghdad, Iraq

^* Corresponding author: asraa.nadheer88@nahrainuniv.edu.iq

Abstract

Multi-layer perceptron (MLP) algorithms play a critical role in improving the accuracy and effectiveness of heart disease diagnosis in the context of the machine learning research. This paper presents an approach of heart disease prediction involves RReliefF-based feature importance assessment then MLP-based classification of features into three groups based on importance scores is proposed. The study employs three feedforward neural networks to classify effectively the clustered groups. Furthermore, an integrated approach utilizes XGBoost ensemble classification, leveraging boosted ensemble learning to enhance overall classification of the outputs of FNN models. By partitioning Cleveland dataset into 70% training and 30% testing sets creates independent datasets, the incorporation of MLP outputs into the XGBoost model yields satisfied testing performance. The confusion matrix showcases accurate classifications, with 96.67% accuracy, 95.92% sensitivity, and 97.92% precision. The F1-Score, at 96.91%, validates the model's balanced performance in precision and recall. This study exemplifies the efficacy of integrating data processing, feature engineering, and ensemble learning techniques for robust cardiovascular disease prediction, providing a reliable and efficient methodology for healthcare applications.