Robust technique using Novel Artificial Neural Network Classifier for detection and classification of Hepatitis C

Aim: The objective of this review is to compare the accuracy, sensitivity, and specificity of ANN (Artificial Neural Network) and SVM (Support Vector Machine) Classifiers in order to determine the presence of Innovative Hepatitis C Detection using contemporary techniques. Materials and Methods: Data for this study were gathered via the Kaggle website. Sample sizes were calculated to be
$\boldsymbol{(\mathrm{N}=22)}$
for Artificial Neural Network Classifier and
$\boldsymbol{(\mathrm{N}=22)}$
for Support Vector Machine Classifier, with an alpha error-threshold value of 0.05, enrollment ratio of 0.1, 95% confidence interval,
$\mathbf{G}$
power of 80%, and total sample size. The accuracy, sensitivity, and specificity were determined using matlab programming and a common data set. Results: SPSS software is used to compare accuracy, sensitivity, and specificity using an independent sample t-test. The difference between ANN and SVM classifiers is statistically significant. In contrast to ANN, the SVM performed well with
$\boldsymbol{\mathrm{p}=0.662},\boldsymbol{\mathrm{p} > 0.05.}$
. accuracy (0.40%),
$\boldsymbol{\mathrm{p}=0.473, \mathrm{p} > 0.05-}$
sensitivity (0.40
^%
), and
$\boldsymbol{\mathrm{p}=0.001, \mathrm{p} < 0.05}$
-specificity (0.510/0).Conclusion: SVM exhibited prosperous accuracy, sensitivity, specificity than ANN to predict Innovative Hepatitis C Detection in a rapid way.