Early stage diabetes prediction using decision tree-based ensemble learning model
Author:
ŞEN Özge1ORCID, BOZKURT KESER Sinem2ORCID, KESKİN Kemal2ORCID
Affiliation:
1. Eskişehir Osmangazi University, Faculty of Engineering, Department of Computer Engineering, 26040, Eskişehir/Turkey 2. Eskişehir Osmangazi University, Faculty of Engineering, Department of Electrical and Electronics Engineering, 26040, Eskişehir/Turkey
Abstract
Diabetes is a lifelong disease that has undesirable effects on various organs, such as long-term organ damage, functional disorder, and finally failure of the organ. Diabetes must be treated under the supervision of a doctor. Diabetes is known as a disease that can be seen in many people today and is becoming widespread due to life conditions. If a person with diabetes does not receive any treatment at an early stage, the patient's body can react with serious complications. In addition to the medical methods used in the diagnosis of diabetes, this disease can be detected by an artificial intelligence approach. This research aims to establish the most influential variable among the many variables causing diabetes and to design a model that will predict diabetes to help doctors analyze the disease with selected machine learning methods. In this study, Decision Tree, Bagging with Decision Tree, Random Forest and Extra Tree algorithms were used for the proposed model and the highest accuracy values were obtained with the Extra Trees algorithm with 99.2%.
Publisher
International Advanced Researches and Engineering Journal
Subject
Pharmacology (medical)
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