Novel Method for classification of Hepatitis C Using Support Vector Machine Classifier

Aim: The aim of this study is to figure out the presence of Novel Hepatitis C Detection using modern classifiers, and comparing the accuracy, sensitivity, specificity between SVM (Support Vector Machine) and K-NN (K-Nearest Neighbour) Classifiers. Materials and Methods: In this study, data was gathered via the kaggle website. According to clinicalc.com, samples were taken into account as
$\boldsymbol{(\mathrm{N}=22)}$
for SVM and
$\boldsymbol{(\mathrm{N}=22)}$
for K-NN, with the total sample size being determined using the following parameters: enrollment ratio of 0.1, 95% confidence interval, G power of 80%, and alpha error-threshold value of 0.05. With a standard data set, the accuracy, sensitivity, and specificity were calculated using MATLAB. Results: Independent sample t test SPSS software compares accuracy, sensitivity, and specificity. Between the K-Nearest Neighbor Classifier and Support Vector Machine Classifier, there is a statistically significant difference. In comparison to SVM, the K-NN performed better with
$\boldsymbol{\mathrm{p}=0.026}$
, p<0.05 accuracy (0.42%),
$\mathbf{p=0.021}$
, p<0.05 sensitivity (0.43%), and
$\boldsymbol{\mathrm{p}=0.001, \mathrm{p} < 0.05}$
specificity (0.43%). Conclusion: K-NN showed better accuracy, sensitivity, specificity than SVM to predict Novel Hepatitis C Detection in a faster way.