High-Resolution Systems for Automated Diagnosis of Hepatitis

This study aims to optimize the accuracy of diseases diagnosis, where many studies have been conducted to challenge the highest diagnostic accuracy of hepatitis disease because the early and correct diagnosis increases the chance of saving the patient's life from this deadly disease. Therefore,...

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Bibliographic Details
Published in2018 Third Scientific Conference of Electrical Engineering (SCEE) pp. 39 - 44
Main Authors Abdullah, Hadeel N., Abd, Bassam H., Muhi, Sara H.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2018
Subjects
Hepatitis diagnosis
K-nearest neighbor
Medical Expert System
multilayer perceptron
support vector machine
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Summary:This study aims to optimize the accuracy of diseases diagnosis, where many studies have been conducted to challenge the highest diagnostic accuracy of hepatitis disease because the early and correct diagnosis increases the chance of saving the patient's life from this deadly disease. Therefore, in this paper, we have done a good test for three classifications, namely: support vector machine (SVM), multilayer perceptron (MLP) and K-nearest neighbor (KNN). The accuracy of the KNN overcome the rest of the classifier with 100% accuracy for the diagnosis of hepatitis disease. We used the same division of data used in previous works for a fair comparison using the datasets gotten from the UCI machine learning database, with 19 features. This result is the best yet.
DOI:10.1109/SCEE.2018.8684154