Design of Diabetes Prediction Interface Using E-ss and Classification Tree Algorithm
Abstract
Diabetes was a chronic disease that continued to increase globally, making early detection essential to reduce long-term complications. This study aimed to develop a desktop-based diabetes prediction system that provided fast and simple classification results for medical personnel and individual users. The system used the entropy-based subset selection (E-ss) method to choose the most relevant attributes and a classification tree to classify the risk. The dataset from the National Institute of Diabetes and Digestive and Kidney Diseases, contained 768 patient records with attributes such as number of pregnancies, glucose level, blood pressure, and other risk factors. The E-ss process produced three attributes with the highest information scores, namely body mass index (BMI), blood pressure, and triceps skinfold thickness. These three attributes were then used as input to the classification tree model to generate diabetes risk predictions. Cross-validation testing showed an accuracy of up to 78.95%. These findings indicated that E-ss feature reduction helped maintain prediction performance while improving computational efficiency. This system was expected to serve as a practical and reliable diagnostic tool.
Keywords
References
W. H. Organization, “Diabetes,” WHO. Accessed: Aug. 02, 2025. [Online]. Available:https://www.who.int/health-topics/diabetes
I. D. Federation, “The Diabetes Atlas,” IDF. Accessed: Aug. 02, 2025. [Online]. Available: https://diabetesatlas.org/
Y. Liu et al., “Performance of a prediabetes risk prediction model: A systematic review,” Heliyon, vol. 9, no. 5, p. e15529, 2023, doi: 10.1016/j.heliyon.2023.e15529.
J. Zhang, Z. Zhang, K. Zhang, X. Ge, R. Sun, and X. Zhai, “Early detection of type 2 diabetes risk: limitations of current diagnostic criteria,” Front. Endocrinol. (Lausanne)., vol. 14, p. 1260623, 2023, doi: 10.3389/fendo.2023.1260623.
B. T. Jijo and A. M. Abdulazeez, “Classification Based on Decision Tree Algorithm for Machine Learning,” J. Appl. Sci. Technol. Trends, vol. 2, no. 1, pp. 20–28, 2021, doi: 10.38094/jastt20165.
T. Dudkina, I. Meniailov, K. Bazilevych, S. Krivtsov, and A. Tkachenko, “Classification and prediction of diabetes disease using decision tree method,” in CEUR Workshop Proceedings, CEUR-WS.org, 2021, pp. 163–172.
O. O. Oladimeji, A. Oladimeji, and O. Oladimeji, “Classification models for likelihood prediction of diabetes at early stage using feature selection,” Appl. Comput. Informatics, vol. 20, no. 3–4, 2024, doi: 10.1108/ACI-01-2021-0022.
V. Pratama and J. Tjen, “Entropy-based subset selection principal component analysis for diabetes risk factor identification,” J. Emerg. Investig., vol. 6, no. 1, pp. 1–4, 2023, doi: 10.59720/23-015.
J. Tjen, F. Smarra, and A. D’Innocenzo, “An entropy-based sensor selection algorithm for structural damage detection,” in IEEE International Conference on Automation Science and Engineering, 2020, pp. 1566–1571. doi: 10.1109/CASE48305.2020.9216828.
F. Smarra, J. Tjen, and A. D’Innocenzo, “Learning methods for structural damage detection via entropy-based sensors selection,” Int. J. Robust Nonlinear Control, vol. 32, no. 10, pp. 1566–1571, 2022, doi: 10.1002/rnc.6124.
Y. Sun et al., “Analysis of continuous glucose monitoring data in patients with type 2 diabetes mellitus based on entropy analysis,” in Proceedings of SPIE, SPIE – The International Society for Optics and Photonics, 2024, p. 132701C. doi: 10.1117/12.3045353.
B. A. C. Permana, R. Ahmad, H. Bahtiar, A. Sudianto, and I. Gunawan, “Classification of diabetes disease using decision tree algorithm (C4.5),” J. Phys. Conf. Ser., vol. 1869, no. 1, p. 012082, 2021, doi: 10.1088/1742-6596/1869/1/012082.
P. Achenbach et al., “A classification and regression tree analysis identifies subgroups of childhood type 1 diabetes,” eBioMedicine, vol. 82, p. 104118, 2022, doi: 10.1016/j.ebiom.2022.104118.
N. Ahmed et al., “Machine learning based diabetes prediction and development of smart web application,” Int. J. Cogn. Comput. Eng., vol. 2, no. December, pp. 229–241, 2021, doi: 10.1016/j.ijcce.2021.12.001.
M. Akturk, “Diabetes Dataset,” Kaggle. Accessed: Aug. 07, 2025. [Online]. Available: https://www.kaggle.com/datasets/mathchi/diabetes-data-set/data
K. Larsen et al., “Developing a User-Centered Digital Clinical Decision Support App for Evidence-Based Medication Recommendations for Type 2 Diabetes Mellitus: Prototype User Testing and Validation Study,” JMIR Hum. Factors, vol. 9, no. 1, p. e33470, 2022, doi: 10.2196/33470.
A. M. Posonia, S. Vigneshwari, and D. J. Rani, “Machine learning based diabetes prediction using decision tree J48,” in Proceedings of the 3rd International Conference on Intelligent Sustainable Systems, ICISS 2020, 2020, pp. 498–502. doi: 10.1109/iciss49785.2020.9316001.
R. Saxena, S. K. Sharma, M. Gupta, and G. C. Sampada, “A Novel Approach for Feature Selection and Classification of Diabetes Mellitus : Machine Learning Methods,” Comput. Intell. Neurosci., vol. 2022, p. 3820360, 2022, doi: 10.1155/2022/3820360.
R. Sistem, M. D. Anasanti, K. Hilyati, and A. Novtariany, “Exploring feature selection techniques on Classification Algorithms for Predicting Type 2 Diabetes at Early Stage,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 6, no. 5, pp. 832–839, 2022, doi: 10.29207/resti.v6i5.4419.
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