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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Ramadanti, Elisa
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Health Professions Materials Science & Nanotechnology

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Diabetes Disease Detection Classification Using Light Gradient Boosting (LightGBM) With Hyperparameter Tuning Ramadanti, Elisa; Aprilya Dinathi, Devi; christianskaditya; Chandranegara, Didih Rizki
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.13530

Abstract

Diabetes is a condition caused by an imbalance between the need for insulin in the body and insufficient insulin production by the pancreas, causing an increase in blood sugar concentration. This study aims to find the best classification performance on diabetes datasets with the LightGBM method. The dataset used consists of 768 rows and 9 columns, with target values of 0 and 1. In this study, resampling is applied to overcome data imbalance using SMOTE and perform hyperparameter optimization. Model evaluation is performed using confusion matrix and various metrics such as accuracy, recall, precision and f1-score. This research conducted several tests. In hyperparameter optimization tests using GridSearchCV and RandomSearchCV, the LightGBM method showed good performance. In tests that apply data resampling, the LightGBM method achieves the highest accuracy, namely the LightGBM method with GridSearchCV optimization with the highest accuracy reaching 84%, while LightGBM with RandomSearchCV optimization reaches 82% accuracy.
Diabetes Detection Using Extreme Gradient Boosting (XGBoost) with Hyperparameter Tuning Dinanthi, Devi Aprilya; Ramadanti, Elisa; Aditya, Christian Sri Kusuma; Chandranegara, Didih Rizki
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 6 No. 2 (2024): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/qr3hw926

Abstract

Diabetes is a serious condition that can lead to fatal complications and death due to metabolic disorders caused by a lack of insulin production in the body. This study aims to find the best classification performance on diabetes dataset using Extreme Gradient Boosting (XGBoost) method. The dataset used has 768 rows and 9 columns, with target values of 0 and 1. In this study, resampling is applied to overcome data imbalance using SMOTE, and hyperparameter optimization is performed using GridSearchCV and RandomSearchCV. Model evaluation was performed using confusion matrix as well as metrics such as accuracy, precision, recall, and F1-score. The test results show that the use of GridSearchCV and RandomSearchCV for hyperparameter tuning provides good results. The application of data resampling also managed to improve the overall model performance, especially in the XGBoost method that has been optimized using GridSearchCV, which achieved the highest accuracy of 85%, while XGBoost with RandomSearchCV optimization showed 83% accuracy performance.
Co-Authors Aprilya Dinathi, Devi Chandranegara, Didih Rizki Christian Sri kusuma Aditya, Christian Sri kusuma christianskaditya Dinanthi, Devi Aprilya