Heart Failure Patient Survival Analysis with Multi Kernel Support Vector Machine

Abstract

Heart failure (HF) is an intercontinental pandemic influencing in any event 26 million individuals globally and is expanding in commonness. HF healthiness consumptions are extensive and will increment significantly with a maturing populace. As per the World Health Organization (WHO), Cardiovascular diseases (CVDs) are the major reason for all-inclusive death, taking an expected 17.9 million lives per year. CVDs are a class of issues of the heart, blood vessels and include coronary heart sickness, cerebrovascular illness, rheumatic heart malady, and various other conditions. In the medical care industry, a lot of information is as often as possible created. Nonetheless, it is frequently not utilized adequately. The information shows that the produced picture, sound, text, or record has some shrouded designs and their connections. Devices used to remove information from these data sets for clinical determination of illness or different reasons for existing are more uncommon. 4 cases out of 5 CVD dying are due to heart attacks and strokes, 33% of these losses of life happen roughly in peoples under 70 year of age. In the current work, we have tried to predict the survival chances of HF sufferers using methods such as attribute selection (scoring method) & classifiers (machine learning). The scoring methods (SM) used here are the Gini Index, Information Gain, and Gain Ratio. Correlation-based feature selection (CFS) with the best first search (BFS) strategy for best attribute selection (AS). We have used multi-kernel support vector machine (MK-SVM) classifiers such as Linear, Polynomial, radial base function (RBF), Sigmoid. The classification accuracy (CA) we received using SM is as follows: SVM (Linear with 80.3%, Polynomial with 86.6%, RBF with 83.6%, Sigmoid with 82.3%) and by using CFS-BFS method are as follows: SVM (Linear with 79.9%, Polynomial with 83.3%, RBF and Sigmoid with 83.6%).

Quantum Fuzzy Support Vector Machine for Binary Classification

“…This hyperplane is used to classify new data sets. There are currently several types of SVM, such as twin SVM [1], Gaussian SVM [2], multi-kernel SVM [3] and so on. Furthermore, SVM has been widely applied in a variety of fields, for example, handwritten hindi character recognition [4], face recognition [5], network intrusion detection [6] and breast cancer diagnosis [7], and so on.…”