Performance Analysis of Multiclass Support Vector Machine Classification for Diagnosis of Coronary Heart Diseases

Abstract

Automatic diagnosis of coronary heart disease helps the doctor to support in decision making a diagnosis. Coronary heart disease have some types or levels. Referring to the UCI Repository dataset, it divided into 4 types or levels that are labeled numbers 1-4 (low, medium, high and serious). The diagnosis models can be analyzed with multiclass classification approach. One of multiclass classification approach used, one of which is a support vector machine (SVM). The SVM use due to strong performance of SVM in binary classification. This research study multiclass performance classification support vector machine to diagnose the type or level of coronary heart disease. Coronary heart disease patient data taken from the UCI Repository. Stages in this study is preprocessing, which consist of, to normalizing the data, divide the data into data training and testing. The next stage of multiclass classification and performance analysis. This study uses multiclass SVM algorithm, namely: Binary . The experiment results showed that the multiclass SVM classification algorithm with the algorithm BT-SVM, OAA-SVM and the ECOC-SVM,gave the highest Recall in the diagnosis of type or healthy level with a value above 90%, precision 82.143% and 86.793% F-measure,. For all kinds of algorithms, except binary OAA-SVM algorithm gave the highest recall 0.0% for the type or level of sickhigh and sick-serious, and ECOC-SVM algorithm gave the highest recall 0.0% for sick-medium and sickserious. While the type or level other, the performance of recall, precision and F-measure between 20% -30%,. The conclusion that can be drawn is that the approach to the multiclass classification algorithm BT-SVM, OAO-SVM, DDAG-SVM to diagnosis the type or level of coronary heart disease provides better performance, than the binary classification approach.

A Powerful Paradigm for Cardiovascular Risk Stratification Using Multiclass, Multi-Label, and Ensemble-Based Machine Learning Paradigms: A Narrative Review

“…The statistical distributions derived from the selected studies are shown in Figure 2 The percentage of studies for each of the three kinds of CVD risk prediction had the following distributions: multiclass (26%) [69][70][71][72][73][74][75][76][77][78][79][80][81][82], multi-label (15%) [83][84][85][86][87][88][89][90], and ensemble (59%) [80, (Figure 2a). Several different kinds of risk classes were identified in multiclass CVD framework, namely binary (65%), tertiary (22%), quaternary (6%), and greater than quaternary (7%) (Figure 2b).…”

“…It was found that almost 82% of MLbased CVD studies performed feature selection for risk prediction whereas only 18% [69,70,73,75,83,94,96,110,120] did not perform it. For the ML-based multi-label CVD (Figure 2d), the total number of GT's used for each study were as follows and given in the ground braces: Venkatesh et al The percentage of studies for each of the three kinds of CVD risk prediction had the following distributions: multiclass (26%) [69][70][71][72][73][74][75][76][77][78][79][80][81][82], multi-label (15%) [83][84][85][86][87][88][89][90], and ensemble (59%) [80, (Figure 2a). Several different kinds of risk classes were identified in multiclass CVD framework, namely binary (65%), tertiary (22%), quaternary (6%), and greater than quaternary (7%) (Figure 2b).…”

“…The types of covariates considered for the multiclass systems were OBBM [71,76,80,82], LBBM [71,76,80,82], CUSIP [76,80] for office-based setups (Table 1: row 1-5), and Electrocardiogram (ECG) [79,81,82], PCG [77], Acceleration Plethysmogram (APG) [78] signals for cardiac stress test laboratories (Table 1: row 6-9), and coronary artery calcium (CAC) for CTbased CVD models [135]. The ground truths considered for CVD risk assessment (Table 1) were death [80], coronary heart disease (CHD) [82], chronic heart conditions (CHC) [79], cardiovascular event (CVE) [76], sudden cardiac death (SCD) [72], heart failure (HF), myocardial infarction (MI) [75], coronary artery calcification (CAC) score [69], fatal/non-fatal CVD [73], joint CVD and diabetes [70]. Note that these gold standard choices along with AI attributes, scientific and clinical validations are key to preventing bias in AI.…”

Interpretation of Clinical Data Based on C4.5 Algorithm for the Diagnosis of Coronary Heart Disease

“…In the black-box approach the system cannot explain the relationship between the input and output attribute, which can be understood by clinicians. Research interpretation systems for diagnosis using the black-box approach include those using neural networks [ 2 ], support vector machine (SVM) [ 3 ], K-star [ 4 ] and naive Bayesian [ 3 ]. The non-black-box approaches in clue those using C4.5 algorithms and fuzzy inference systems [ 5 6 7 ].…”

“…This data should be interpreted as indicating one of the four types/levels of coronary heart disease, but instead, it is interpreted as indicting that the patient is healthy. The results of research conducted by Wiharto et al [ 3 ], which tested several types of multiclass SVM algorithms and used the UCI dataset repository [ 12 ], showed good performance for the type/level with a large amount of training data.…”