Heart Disease Classification Using Machine Learning by Modified Support Vector Machine and Improved Ensemble Extreme Gradient Boosting (XGBoost)

Abstract

 Heart disease has become a major problem recently which lowers people standard of living. In the modern world, heart disease deaths have grown to be a serious issue. Heart disease claims the life of almost one person every minute. The next two conditions on the list are breast cancer and chronic kidney disease (CKD). The majority of the time, a complex combination of pathological and clinical evidence is used to make the diagnosis. This paper's goal is to use image categorization to forecast cardiac disease. Preprocessing, Feature Selection (FS), and Machine Learning (ML) classification techniques are all used in the suggested methodology. Initially, preprocessing is done using Fuzzy K-Nearest Neighbour (FKNN). Adaptive Kernel Density (AKD) and Quantum Teaching Learning based Optimization (QTLO) are used for FS. Lastly, Improved Ensemble Extreme Gradient Boosting (XGBoost) and Modified Support Vector Machines (MSVM) classify samples where accuracy, precision, recall, and f-measure are the outcomes of FS and categorization techniques. Experimental results demonstrated that the suggested approaches outperform the current system with superior quality features. In comparison to the current methods, the estimated time period and error rate are also lower. Using suggested approaches, the datasets for breast cancer and chronic renal disease are also compared, producing superior results.