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Development and validation of a GBDT-based for detection of diabetes
- 1 Hefei No.8 Senior High School, Anhui, China
* Author to whom correspondence should be addressed.
Abstract
Improved early detection and intervention can lessen the substantial health and financial burden of type 2 diabetes (T2D). In order to achieve early prediction of diabetes, an approach for diabetes prediction based on Gradient Boosting Decision Tree (GBDT) was proposed in the present study. The feature selection approach based on GBDT was designed to identify the most relevant features, where GBDT builds a powerful model by combining multiple weak classifiers, which can reduce the risk of overfitting and improve the model generalization ability. Condition prediction was performed based on GBDT and the filtered features, and the truncation value of the feature was calculated. The experimental results in the paper showed that the Area Under the Curve (AUC) value of GBDT was 0.9788, which was a big improvement compared with other studies; the AUC value based on glycated hemoglobin level was 0.7307, and the cutoff value of glycated hemoglobin level was about 6.8, which was very accurate. The prediction of diabetes based on GBDT can help patients to understand whether they have diabetes initially based on their own glycated hemoglobin values, and it can also help clinicians to make more objective judgments in clinical diagnosis in order to judge the patient's situation and subsequent monitoring of the condition, making an excellent contribution to the control of the patient's condition.
Keywords
GBDT, Type 2 diabetes, Machine learning, Cutoff value of glycated hemoglobin level
[1]. International Diabetes Federation 2021 IDF Diabetes Atlas, Brussels, Belgium: International Diabetes Federation.
[2]. Wendy K Chung, Karel Erion, Jose C Florez, et al 2020 Precision medicine in diabetes: a Consensus Report from the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD). Diabetologia. 63 1671–93
[3]. Henock M Deberneh and Intaek Kim 2021 Prediction of Type 2 Diabetes Based on Machine Learning Algorithm. Int J Environ Res Public Health. 18 3317
[4]. Somayeh Sadeghi, Davood Khalili, Azra Ramezankhani, Mohammad Ali Mansournia and Mahboubeh Parsaeian 2022 Diabetes mellitus risk prediction in the presence of class imbalance using flexible machine learning methods. BMC Med Inform Decis Mak. 22 36
[5]. Bu F, Deng XH, Zhan NN, Cheng H, Wang ZL, Tang L, Zhao Y, Lyu QY 2023 Development and validation of a risk prediction model for frailty in patients with diabetes BMC Geriatr. 23 172
[6]. Pripp AH 2018 Pearson's or Spearman's correlation coefficients Tidsskr Nor Laegeforen. 138
[7]. Stoltzfus JC 2011 Pearson's or Spearman's correlation coefficients Acad Emerg Med. 18 1099-104
[8]. Al Fryan LH, Shomo MI, Alazzam MB and Rahman MA 2022 Processing Decision Tree Data Using Internet of Things (IoT) and Artificial Intelligence Technologies with Special Reference to Medical Application. Biomed Res Int. 2022 8626234
[9]. Rigatti SJ 2017 Random Forest. J Insur Med. 47 31-39
[10]. Hatwell J, Gaber MM and Atif Azad RM 2020 Ada-WHIPS: explaining AdaBoost classification with applications in the health sciences. BMC Med Inform Decis Mak 20 250
[11]. Davagdorj K, Pham VH, Theera-Umpon N and Ryu KH 2020 XGBoost-Based Framework for Smoking-Induced Noncommunicable Disease Prediction. Int J Environ Res Public Health. 17 6513
[12]. Farrokh Habibzadeh, Parham Habibzadeh, and Mahboobeh Yadollahie 2016 On determining the most appropriate test cut-off value: the case of tests with continuous results. Biochem Med (Zagreb). 26 297–307
Cite this article
Wu,X. (2024). Development and validation of a GBDT-based for detection of diabetes. Applied and Computational Engineering,67,115-121.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 2nd International Conference on Software Engineering and Machine Learning
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