Application of Random Forest on C5.0 Algorithm for Diabetes Mellitus Disease Classification in RSUD Tora Belo Sigi District

DOI

10.2991/978-94-6463-520-1_15How to use a DOI?

Keywords

C5.0; Decision Tree; Diabetes Mellitus; Classification; Random Forest

Abstract

In 2021, Sigi Regency is the area with the highest level of diabetes mellitus in Central Sulawesi Province, as evidenced by the number of patients at RSUD Tora Belo which continues to increase where in 2020 there were 466 patients and in 2021 it increased to 526 patients. Accurate classification of people who have positive or negative laboratory test results for diabetes mellitus is important to get the right treatment. The purpose of this study is to classify the status of people who have positive or negative laboratory test results for diabetes using random forest applied to the C5.0 algorithm. The results obtained are that the Low Density Lipoprotein variable is the most important variable in the classification with a mean decrease gini value of 40.701691 so that the main factor that causes a person to suffer from diabetes mellitus is the Low Density Lipoprotein variable with a classification accuracy of 88.17%.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Nur Intan
AU  - Mohammad Fajri
AU  - Hartayuni Sain
PY  - 2024
DA  - 2024/12/05
TI  - Application of Random Forest on C5.0 Algorithm for Diabetes Mellitus Disease Classification in RSUD Tora Belo Sigi District
BT  - Proceedings of the 5th International Seminar on Science and Technology (ISST 2023)
PB  - Atlantis Press
SP  - 98
EP  - 104
SN  - 2352-541X
UR  - https://doi.org/10.2991/978-94-6463-520-1_15
DO  - 10.2991/978-94-6463-520-1_15
ID  - Intan2024
ER  -