A Cloud-Based Optimized Ensemble Model for Risk Prediction of Diabetic Progression—An Azure Machine Learning Perspective
The application of Machine Learning for predictive analysis in healthcare, particularly for diseases like diabetes, has proven highly beneficial. This study introduces an optimized Light Gradient-Boosting Machine (Light GBM) and K-Nearest Neighbour (KNN) based ensemble algorithm for predicting diabe...
Saved in:
Main Authors: | V. K. Daliya, T. K. Ramesh |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2025-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10836739/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A flexible multi-scale approach for downscaling GRACE-derived groundwater storage anomaly using LightGBM and random forest in the Tashk-Bakhtegan Basin, Iran
by: Arezo Mohtaram, et al.
Published: (2025-02-01) -
Toward accurate prediction of carbon dioxide (CO2) compressibility factor using tree-based intelligent schemes (XGBoost and LightGBM) and equations of state
by: Behnam Amiri-Ramsheh, et al.
Published: (2025-03-01) -
Intrusion detection in metaverse environment internet of things systems by metaheuristics tuned two level framework
by: Milos Antonijevic, et al.
Published: (2025-01-01) -
An Ensemble Based Machine Learning Classification for Automated Glaucoma Detection
by: Digvijay J. Pawar, et al.
Published: (2024-12-01) -
Application of an improved LightGBM hybrid integration model combining gradient harmonization and Jacobian regularization for breast cancer diagnosis
by: Xiaoyan Sun
Published: (2025-01-01)