Leveraging Generative AI and ML For Disease-Specific Diagnostic Precision: A Comparative Analysis On Parkinson’s, Heart Disease, and Diabetes

DOI

10.2991/978-94-6463-858-5_104How to use a DOI?

Keywords

Parkinson’s Disease; Heart Disease; Diabetes; Generative Artificial Intelligence; Machine Learning; Predictive Algorithms; Diagnostic Precision; Clinical Decision-Making

Abstract

Healthcare predictive modeling has been transformed by combining generative artificial intelligence (Gen AI) and machine learning (ML), which provide complex computational frameworks for precise disease prognosis. Leveraging Gen AI’s ability to synthesize complex, high-dimensional medical data and ML’s advanced pattern recognition capabilities, our research focuses on precisely predicting critical diseases, including cardiovascular conditions, diabetes mellitus, and Parkinson’s disease. These technologies empower the development of predictive algorithms that can model intricate dependencies within biomedical datasets, enhancing diagnostic precision and clinical decision-making. Using a strong comparative analysis framework, our research systematically assesses the performance of numerous machine learning techniques, such as logistic regression, decision trees, random forests, support vector machines, and deep neural networks. We conducted an extensive literature survey against prior studies, extracting insights into algorithmic efficiencies and constraints. Key results indicate that algorithmic performance is disease-specific, prompting the development of an integrated, adaptive backend architecture. This system intelligently deploys the most effective algorithm for each disease type, optimizing diagnostic outcomes and computational resources. Our research delivers a novel, data-driven strategy for disease-specific model optimization, with significant implications for deploying AI-driven predictive tools in personalized healthcare. The findings demonstrate the promise of Gen AI and ML in redefining clinical diagnostics, paving the way for advancements in precision medicine.

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  - Aaditya Raj Gupta
AU  - Anukrati Agarwa
AU  - Ayuska Singh
AU  - Harsiddhi Singh Dev
PY  - 2025
DA  - 2025/11/04
TI  - Leveraging Generative AI and ML For Disease-Specific Diagnostic Precision: A Comparative Analysis On Parkinson’s, Heart Disease, and Diabetes
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 1248
EP  - 1260
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_104
DO  - 10.2991/978-94-6463-858-5_104
ID  - Gupta2025
ER  -