Machine Learning Approach for Diabetes Detection Using Fine-Tuned XGBoost Algorithm

Authors

  • Aga Maulana Department of Informatics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
  • Farassa Rani Faisal Department of Informatics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
  • Teuku Rizky Noviandy Department of Informatics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
  • Tatsa Rizkia General Practitioner, School of Medicine, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
  • Ghazi Mauer Idroes Department of Occupational Health and Safety, Faculty of Health Sciences, Universitas Abulyatama, Aceh Besar 23372, Indonesia
  • Trina Ekawati Tallei Department of Biology, Faculty of Mathematics and Natural Sciences, Sam Ratulangi University, Manado 95115, North Sulawesi, Indonesia
  • Mohamed El-Shazly Department of Pharmacognosy, Faculty of Pharmacy, Ain-Shams University, Cairo 11566, Egypt
  • Rinaldi Idroes Department of Chemistry, Faculty of Mathematics and Natural Sciences Universitas Syiah Kuala, Banda Aceh 23111, Indonesia

DOI:

https://doi.org/10.60084/ijds.v1i1.72

Keywords:

Classification, Feature importance, Pima Indian, Hyperparameter tuning, Supervised learning

Abstract

Diabetes is a chronic condition characterized by elevated blood glucose levels which leads to organ dysfunction and an increased risk of premature death. The global prevalence of diabetes has been rising, necessitating an accurate and timely diagnosis to achieve the most effective management. Recent advancements in the field of machine learning have opened new possibilities for improving diabetes detection and management. In this study, we propose a fine-tuned XGBoost model for diabetes detection. We use the Pima Indian Diabetes dataset and employ a random search for hyperparameter tuning. The fine-tuned XGBoost model is compared with six other popular machine learning models and achieves the highest performance in accuracy, precision, sensitivity, and F1-score. This study demonstrates the potential of the fine-tuned XGBoost model as a robust and efficient tool for diabetes detection. The insights of this study advance medical diagnostics for efficient and personalized management of diabetes.

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Published

2023-08-22

How to Cite

Maulana, A., Faisal, F. R., Noviandy, T. R., Rizkia, T., Idroes, G. M., Tallei, T. E., El-Shazly, M., & Idroes, R. (2023). Machine Learning Approach for Diabetes Detection Using Fine-Tuned XGBoost Algorithm. Infolitika Journal of Data Science, 1(1), 1–7. https://doi.org/10.60084/ijds.v1i1.72

Issue

Vol. 1 No. 1 (2023): September 2023

Section

Articles

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Copyright (c) 2023 Aga Maulana, Farassa Rani Faisal, Teuku Rizky Noviandy, Tatsa Rizkia, Ghazi Mauer Idroes, Trina Ekawati Tallei, Mohamed El-Shazly, Rinaldi Idroes

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