Classification of diabetes disease using decision tree algorithm (C4.5)
Baiq Andriska Candra Permana, Ramli Ahmad, Hariman Bahtiar, Aris Sudianto, Irwan Prasetya Gunawan
Abstract
Abstract Diabetes is one of the most common health problems in the world. Diabetes is also known as “the silent killer” because according to WHO (2016) diabetes increased from 108 million in 1980 to about 422 million adults had diabetes in 2014. If not handled properly, diabetes can become chronic and damage other organs and can cause death. This disease has several symptoms in the patient but evaluating the different factors or symptom variables required to determine which variables are more dominant. This research aims to establish the most influential variable of the many variables causing diabetes mess. We suggest using a data mining decision tree (C4.5) in this paper to forecast diabetes to help doctors analyse the disease sooner. Data mining has carried out various approaches to predict a disease, one of them is the use of c4.5. In this research, produce a decision tree and the result shown that polydipsia play a role in diabetes with accuracy 90.38 %. One of the most dominant signs of diabetics is the sign of polydipsia.