Modeling Early-stage Diabetes Mellitus using an Ensemble Learning Approach
Pemodelan Deteksi Dini Diabetes Mellitus menggunakan Pendekatan Ensemble Learning
DOI:
https://doi.org/10.21070/ups.4219Keywords:
prediction of early-stage, Diabetes Mellitus, RapidMiner, Classification, Random ForestAbstract
Diabetes mellitus is characterized by hyperglycemia caused by the pancreas's inability to produce insulin properly. Diabetes has early-stage symptoms that can be used as a benchmark for determining whether a person has diabetes mellitus or not. Based on data from Sidoarjo Regional General Hospital, diabetes cases are the fourth most common of the 10 biggest diseases in Sidoarjo Regional General Hospital. The purpose of this research is to detect early symptoms of type 2 diabetes mellitus, Data annotation is performed by proficient paramedics within their respective fields. This research uses the ensemble learning classification method with Rapidminer tools, conducts training and testing tests with a ratio of 60:40 on split data operators, and adds performance to produce accuracy values. The results obtained in the form of evaluation results with a Random Forest accuracy rate of 87.30%, where the accuracy level can be categorized as excellent classification,.
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