Machine Learning-Based Model for Predicting Insulin Dosing in Diabetic Patients

DOI

10.2991/978-94-6463-858-5_230How to use a DOI?

Keywords

Machine Learning; Diabetes Prediction; Insulin Dosing; PIMA Diabetes; UCI Insulin Dosage; Classification; Gradient Boosting; Disease Management

Abstract

Accurate diagnosis of diabetes and particular willpower of insulin dosage are vital for most appropriate management of the circumstance. This research utilizes machine learning, using the Gradient Boosting Classifier to predict diabetes and Logistic Regression to decide insulin dosage. The PIMA Diabetes dataset and the UCI Insulin Dosage dataset are the premise for model training. Gradient Boosting attains an accuracy of 98% in diabetes detection, rendering it a reliable option for class. users may input take a look at effects devoid of class labels, wherein the system to start with determines the presence of diabetes (zero indicating absence of diabetes, 1 indicating presence of diabetes). Upon the detection of diabetes, the model forecasts the important insulin dosage. visual representations elucidate the connection between critical tendencies and the prevalence of diabetes, imparting sizeable insights for healthcare practitioners. The incorporation of a particular machine learning model improves early detection and tailor- made remedy, facilitating superior illness manipulate. The findings confirm the efficacy of Gradient Boosting in diabetes categorization, illustrating the promise of AI-pushed solutions in healthcare for boosting diagnosis and treatment techniques.

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.

Download article (PDF)

TY  - CONF
AU  - O. Sampath
AU  - Pushpala Deepthi
PY  - 2025
DA  - 2025/11/04
TI  - Machine Learning-Based Model for Predicting Insulin Dosing in Diabetic Patients
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 2755
EP  - 2763
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_230
DO  - 10.2991/978-94-6463-858-5_230
ID  - Sampath2025
ER  -