Data Mining Model For Designing Diagnostic Applications Inflammatory Liver Disease

Abstract

Hepatitis is an infectious disease that is a public health problem that affects morbidity, mortality, public health status, life expectancy, and other socio-economic impacts. Early diagnosis of hepatitis is very important so that it can be treated and treated quickly. In this study, the authors will apply and compare several data mining classification methods, including the C4.5 algorithm, Naïve Bayes, and k-Nearest Neighbor to diagnose hepatitis, 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.
 

Implementasi Metode Naïve Bayes Untuk Klasifikasi Data Blogger

“…Selanjutnya penelitian yang dilakukan oleh (Prayoga, 2018) dan kawan-kawan yang meneliti tentang Diagnosis penyakit hepatitis dengan metode Naïve Bayes. Dari pengujian tingkat akurasi yang dilakukan, diketahui bahwa nilai akurasi yang dihasilkan dengan metode Naïve Bayes cukup baik atau cukup tinggi yakni sebesar 87,50%, namun jika pengujiannya ditambahkan dengan feature selection kemungkinan dapat menghasilkan akurasi yang lebih tinggi [5].…”