Early Disease Prediction Using Artificial Intelligence

DOI

10.2991/978-94-6239-697-5_21How to use a DOI?

Keywords

Artificial Intelligence; Explainable AI; Disease Prediction; Random Forest; SHAP; Preventive Healthcare; Clinical Decision Support

Abstract

Early disease detection reduces mortality and healthcare costs. This study proposes an AI-powered early warning system using age, blood pressure, glucose, and BMI to predict risks for diabetes and heart disease. Random Forest with SHAP-based explainability was selected based on a systematic benchmark of UCI datasets, achieving 78–85% accuracy across studies. The system supports preventive healthcare by delivering interpretable risk alerts to clinicians and patients. Future work includes wearable integration and federated learning for privacy.

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  - Gagan Shankar Verma
AU  - Harwinder Singh Sohal
AU  - Vishal Khanna
PY  - 2026
DA  - 2026/06/04
TI  - Early Disease Prediction Using Artificial Intelligence
BT  - Proceedings of the Conference on Bridging Engineering Disciplines with AI and Machine Learning (BEDAIML 2026)
PB  - Atlantis Press
SP  - 242
EP  - 254
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-697-5_21
DO  - 10.2991/978-94-6239-697-5_21
ID  - Verma2026
ER  -