Enhancing Data Collection Strategies for Optimizing Machine Learning Models in the Early Prediction of Chronic Kidney Disease

DOI

10.2991/978-94-6239-654-8_6How to use a DOI?

Keywords

Chronic Kidney Disease; machine learning; data collection strategies; predictive analytics; early detection; data augmentation; healthcare optimization

Abstract

The Chronic Kidney Disease (CKD) is a global public health problem with asymptomatic progression and significant morbidity. Early detection is important as management can be effective and costs of healthcare be reduced. The objective of this project is to improve data collection methods for early prediction of CKD using machine learning models. We address data quality and representation challenges through data integration, spanning disparate sources of information ranging from electronic health records, demographics, real time monitoring devices, and socio environmental factors. To improve reliability of the model, the advanced preprocessing techniques such as data augmentation and class imbalance mitigation are used. We explore the behavior of various machine learning algorithms including ensemble methods as well as deep learning models to determine which predictive features are most important. Ethical considerations, data privacy, regulatory compliance and so on, are put in emphasis. The proposed framework closes the gap between clinical practices and predictive analytics, resulting in robust and interpretable models for early CKD prediction in diverse population. We contribute toward precision medicine and towards the adoption of proactive healthcare strategies.

Copyright

© 2026 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  - J. Joan Niveda
AU  - R. Yogesh Rajkumar
PY  - 2026
DA  - 2026/04/24
TI  - Enhancing Data Collection Strategies for Optimizing Machine Learning Models in the Early Prediction of Chronic Kidney Disease
BT  - Proceedings of the Global Conference on Sustainable Energy Systems, Smart Electronics and Intelligent Computing (GCSESEIC 2025)
PB  - Atlantis Press
SP  - 59
EP  - 66
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6239-654-8_6
DO  - 10.2991/978-94-6239-654-8_6
ID  - Niveda2026
ER  -