Diabetes Prediction: The Influence of the Model and Feature Weights on the Accuracy Rate
Research Article
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Diabetes Prediction: The Influence of the Model and Feature Weights on the Accuracy Rate

Yiming Gao 1*
1 School of Statistics and Data Science, Capital University of Economics and Business, Beijing, China
*Corresponding author: dorothyyy2005@outlook.com
Published on 5 November 2025
Volume Cover
ACE Vol.204
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-80590-517-2
ISBN (Online): 978-1-80590-518-9
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Abstract

Diabetes is one of the most common diseases that targets the elderly population worldwide. Therefore, early prediction is of crucial significance for intervention treatment. This study focuses on two models: logistic regression and fully connected layers. For the task of predicting diabetes incidence, it compares the impact of having or not having a feature weight strategy on the model's accuracy. The experiment was characterized by clinical physiological indicators, and two types of models were constructed, respectively: a logistic regression model with weights and a model with average weights. The accuracy was evaluated through 5-fold cross-validation. The results show that due to the linear nature of the task, the prediction accuracy of logistic regression is superior to that of the fully connected layer. Moreover, for all model types, the weight strategy can significantly improve the accuracy. This study provides practical references for model selection and feature engineering in diabetes prediction and also offers a theoretical basis for the adaptability of models and weight mechanisms in linear tasks.

Keywords:

Diabetes Prediction, Logistic Regression, Fully Connected Layer, Feature Weight, Accuracy Rate

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Gao,Y. (2025). Diabetes Prediction: The Influence of the Model and Feature Weights on the Accuracy Rate. Applied and Computational Engineering,204,21-28.

References

[1]. Hasan, M. K., Alam, M. A., Das, D., Hossain, E., & Hasan, M. (2020). Diabetes prediction using ensembling of different machine learning classifiers. IEEE Access, 8, 76516-76531.

[2]. Bellazzi, R., & Abu-Hanna, A. (2009). Data mining technologies for blood glucose and diabetes management. Journal of diabetes science and technology, 3(3), 603-612.

[3]. Panwar, M., Acharyya, A., Shafik, R. A., & Biswas, D. (2016, December). K-nearest neighbor based methodology for accurate diagnosis of diabetes mellitus. In 2016 sixth international symposium on embedded computing and system design (ISED) (pp. 132-136). IEEE.

[4]. Ayon, S. I., & Islam, M. M. (2019). Diabetes prediction: a deep learning approach. International Journal of Information Engineering and Electronic Business, 10(2), 21.

[5]. Sisodia, D., & Sisodia, D. S. (2018). Prediction of diabetes using classification algorithms. Procedia computer science, 132, 1578-1585.

[6]. Varma, S., & Simon, R. (2006). Bias in error estimation when using cross-validation for model selection. BMC bioinformatics, 7(1), 91.

[7]. Prusty, S., Patnaik, S., & Dash, S. K. (2022). SKCV: Stratified K-fold cross-validation on ML classifiers for predicting cervical cancer. Frontiers in Nanotechnology, 4, 972421.

[8]. Bhatt, A. R., Ganatra, A., & Kotecha, K. (2021). Cervical cancer detection in pap smear whole slide images using convnet with transfer learning and progressive resizing. PeerJ Computer Science, 7, e348.

[9]. Marcot, B. G., & Hanea, A. M. (2021). What is an optimal value of k in k-fold cross-validation in discrete Bayesian network analysis?. Computational Statistics, 36(3), 2009-2031.

[10]. Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., ... & Dean, J. (2019). A guide to deep learning in healthcare. Nature medicine, 25(1), 24-29.

References

[1]. Hasan, M. K., Alam, M. A., Das, D., Hossain, E., & Hasan, M. (2020). Diabetes prediction using ensembling of different machine learning classifiers. IEEE Access, 8, 76516-76531.

[2]. Bellazzi, R., & Abu-Hanna, A. (2009). Data mining technologies for blood glucose and diabetes management. Journal of diabetes science and technology, 3(3), 603-612.

[3]. Panwar, M., Acharyya, A., Shafik, R. A., & Biswas, D. (2016, December). K-nearest neighbor based methodology for accurate diagnosis of diabetes mellitus. In 2016 sixth international symposium on embedded computing and system design (ISED) (pp. 132-136). IEEE.

[4]. Ayon, S. I., & Islam, M. M. (2019). Diabetes prediction: a deep learning approach. International Journal of Information Engineering and Electronic Business, 10(2), 21.

[5]. Sisodia, D., & Sisodia, D. S. (2018). Prediction of diabetes using classification algorithms. Procedia computer science, 132, 1578-1585.

[6]. Varma, S., & Simon, R. (2006). Bias in error estimation when using cross-validation for model selection. BMC bioinformatics, 7(1), 91.

[7]. Prusty, S., Patnaik, S., & Dash, S. K. (2022). SKCV: Stratified K-fold cross-validation on ML classifiers for predicting cervical cancer. Frontiers in Nanotechnology, 4, 972421.

[8]. Bhatt, A. R., Ganatra, A., & Kotecha, K. (2021). Cervical cancer detection in pap smear whole slide images using convnet with transfer learning and progressive resizing. PeerJ Computer Science, 7, e348.

[9]. Marcot, B. G., & Hanea, A. M. (2021). What is an optimal value of k in k-fold cross-validation in discrete Bayesian network analysis?. Computational Statistics, 36(3), 2009-2031.

[10]. Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., ... & Dean, J. (2019). A guide to deep learning in healthcare. Nature medicine, 25(1), 24-29.

Cite this article

Gao,Y. (2025). Diabetes Prediction: The Influence of the Model and Feature Weights on the Accuracy Rate. Applied and Computational Engineering,204,21-28.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of CONF-MLA 2025 Symposium: Intelligent Systems and Automation: AI Models, IoT, and Robotic Algorithms

ISBN: 978-1-80590-517-2(Print) / 978-1-80590-518-9(Online)
Editor: Hisham AbouGrad
Conference website: https://www.confmla.org/london.html
Conference date: 12 November 2025
Series: Applied and Computational Engineering
Volume number: Vol.204
ISSN: 2755-2721(Print) / 2755-273X(Online)

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