Diagnosis of Hepatitis Virus using Decision Tree and Genetic Algorithm

Khatoon, Mariya; Kumar, Abhay

doi:10.5120/ijca2019919539

Cited by 1 publication

(1 citation statement)

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“…It divides the origin node into binary sections in succession, depending on the scale of genetic impurities (Karthikeyan and Thangaraju, 2013):…”

Section: Regression Tree Generationmentioning

confidence: 99%

An Artificial Intelligence Algorithm to Optimize the Classification of the Hepatitis Type

Khalil¹,

Rada²

2023

JEAS

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Hepatitis is one of the diseases that has become more developed in recent years in terms of the high number of infections. Hepatitis causes inflammation that destroys liver cells, and it occurs as a result of viruses, bacteria, blood transfusions, and others. There are five types of hepatitis viruses, which are (A, B, C, D, E) according to their severity. The disease varies by type. Accurate and early diagnosis is the best way to prevent disease, as it allows infected people to take preventive steps so that they do not transmit the difference to other people, and diagnosis using artificial intelligence gives an accurate and rapid diagnostic result. Where the analytical method of the data relied on the radial basis network to diagnose the disease, in addition to using the classification of the regression tree as well as the use of the genetic algorithm to improve the classification accuracy of both methods and by comparing the methods used to find out the most efficient methods of classification through criteria. Classification error, mean square root error, and average absolute relative error, and concluded that the experimental results are that the methods are good in terms of classification, as they gave results with less classification of error, and that the radial basis network was superior to the classification regression tree, and that the addition of the genetic algorithm led to an improvement classification accuracy. Paper type: Research paper.

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“…It divides the origin node into binary sections in succession, depending on the scale of genetic impurities (Karthikeyan and Thangaraju, 2013):…”

Section: Regression Tree Generationmentioning

confidence: 99%