Multi-gradient boosted adaptive SVM-based prediction of heart disease

Abstract

When it comes to modern death rates, heart disease ranks high. Clinical data analysis has a significant difficulty in the domain of heart disease prediction. For the healthcare business, which generates vast amounts of data, Machine Learning (ML) has proven to be a useful tool for aiding in decision-making and prediction. Recent innovations in various domains of the Internet of Things have also made use of ML approaches. Predicting heart disease using ML approaches is only partially explored in the available research. Therefore, to accurately predict heart illness, we provide a unique Multi-Gradient Boosted Adaptive Support Vector Machine (MBASVM). The Multi gradient boosted adaptive support vector machine (MBASVM), an ensemble meta-algorithm, successfully transforms weak learners into strong learners while removing dataset biases for machine learning algorithms. The boosting approach tries to improve the predictability of cardiac disease. For extracting useful features from data, Kernel-Based Principal Component Analysis (K-PCA) is used. The suggested model’s retrieved data are narrowed down using the Chi-Squared Ranker Search (CRS) approach. Measures of recall, sensitivity, specificity, f1-score, accuracy, and precision are used to evaluate the effectiveness of the suggested technique.Comprehensive testing shows that, when compared to other ways, our methodology performed the best.