Implementation of Soft Voting Ensemble Learning for Early Detection of Type 2 Diabetes Mellitus

Authors

  • Carles Sekolah Tinggi Ilmu Kesehatan Tengku Maharatu
  • Osi Draini STIKES Tengku Maharatu
  • Sabtria Winda Sari
  • Nu'man STIKES Tengku Maharatu
  • M. Khairul Anam Universitas Samudra

DOI:

10.33395/sinkron.v10i4.16740

Keywords:

Diabetes, Machine Learning, GUI, Ensemble Learning, Soft Voting

Abstract

Type 2 Diabetes Mellitus is a chronic disease that requires early detection to minimize the risk of complications. This study aims to develop a machine learning model for the early detection of Type 2 Diabetes Mellitus using an ensemble Soft Voting approach. The proposed model combines three algorithms, namely LightGBM, Random Forest, and Support Vector Machine. The dataset used consists of patient health attributes, including pregnancies, glucose level, blood pressure, skin thickness, insulin level, body mass index (BMI), diabetes pedigree function, and age. The preprocessing stage was conducted to prepare the data before the training process, including data balancing so that each class contained the same number of records, namely 500 diabetes data and 500 non-diabetes data. The evaluation results show that the model achieved an accuracy of 80%, with precision, recall, and f1-score of 0.80 for both classes. In addition, the AUC value of 0.89 indicates that the model has a very good capability in distinguishing between diabetes and non-diabetes classes. The system was also implemented in the form of a Graphical User Interface (GUI) to facilitate patient clinical data input and display risk detection results. Based on these results, the developed Soft Voting model can be used as a practical and sufficiently accurate tool for the early detection of Type 2 Diabetes Mellitus.

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References

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How to Cite

Carles, Draini, O., Sari, S. W. ., Nu’man, & Anam, M. K. (2026). Implementation of Soft Voting Ensemble Learning for Early Detection of Type 2 Diabetes Mellitus. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 10(4). https://doi.org/10.33395/sinkron.v10i4.16740

Issue

Vol. 10 No. 4 (2026): Research Articles October 2026

Section

Articles

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Copyright (c) 2026 Carles, Osi Draini, Sabtria Winda Sari, Nu'man, M. Khairul Anam

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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