Enhanced Prediction of Diabetes Outcomes Using Machine Learning Ensemble Modeling with Hyperparameter Tuning

DOI

10.2991/978-94-6463-700-7_29How to use a DOI?

Keywords

Hyperparameter Tuning; Prediction; Ensemble; Chronic Disease; Model Performance

Abstract

Diabetes is one of the diseases that are engraved with immense challenges to the population and the global health systems. Such factors are essential for enhancing the probability of a better prognosis to work for the advantage of the patient. This paper applies an enhancement of the machine learning technique through ensemble modeling with hyperparametric tuning for predicting diabetes outcomes. For compiling the improved and accurate prediction model, the study incorporates the merits of both the random forest and the gradient boosting models. By fine-tuning the pre-processing and tuning the hyperparameter of the model, the accuracy rate was improved to 96%. The iterative improving technique of gradient boosting and random forest’s ability to deal with high-order feature interactions enabled the model improvement a lot. Besides increasing the accuracy of the prediction, the ensemble technique provides a more reliable and comprehensible model for clinical practice. This approach may also enable medical practitioners to diagnose patients at an early stage and arrange treatments to enhance the patient’s quality and reduce the chance of complications for patients with diabetes. The findings here also apply the benefits of hyper-parameter optimization, thereby enhancing the outcome of a model, while the gathered outcome demonstrates the effectiveness of machine learning ensemble solutions in the medical prediction context.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Ritik Kumar
AU  - Nipun Chawla
AU  - Saket Pandey
AU  - Shaifali Sharma
PY  - 2025
DA  - 2025/04/19
TI  - Enhanced Prediction of Diabetes Outcomes Using Machine Learning Ensemble Modeling with Hyperparameter Tuning
BT  - Proceedings of the International Conference on Advancements in Computing Technologies and Artificial Intelligence (COMPUTATIA-2025)
PB  - Atlantis Press
SP  - 354
EP  - 366
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-700-7_29
DO  - 10.2991/978-94-6463-700-7_29
ID  - Kumar2025
ER  -