Data Mining Model for Designing Diagnostic Applications Inflammatory Liver Disease

Lihat/Buka File Repository

Lihat/Buka File Peer Review

Tanggal

2020-10-01

Abstraksi

The liver is a vital organ for humans. Liver disease is a disorder of every liver function. Early diagnosis of  liver disease  is very important so that it can be treated and treated quickly.  In the medical field, diagnosing inflammatory liver disease has become a rather difficult thing to do. However, there are medical records that store the patient's symptoms. This is certainly very beneficial for medical personnel or doctors. They can use previous medical records as material for making decisions about the patient's disease diagnosis. The conventional manual analysis technique that has been used so far is no longer effective for diagnosis. Along with the development of medical knowledge-based systems, the demand for the use of computer-based knowledge systems as an analytical technique in diagnosing diseases is becoming increasingly important. In this study, researchers will apply and compare several data mining classification methods, including the C4.5 algorithm, Naïve Bayes, and k-Nearest Neighbor to diagnose inflammatory  liver disease, then compare which of the three methods is the most accurate. Based on the results of measuring the performance of the three models using the Cross Validation, Confusion Matrix and ROC Curve methods, it is known that the C4.5 method is the best method with an accuracy of 70.99% and an under the curva (AUC) value of 0.950, then the k-Nearest Neighbor method with accuracy of 67.19% and the value under the curve (AUC) 0.873, then the naïve Bayes method with an accuracy rate of 66.14% and a value under the curve (AUC) of 0.742

URI
https://jurnal.polgan.ac.id/index.php/sinkron/article/view/10589/393

Bidang ilmu
Data Mining

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