Leveraging Artificial Intelligence for Multi-Modal Learning in Healthcare Applications

DOI

10.2991/978-94-6463-700-7_25How to use a DOI?

Keywords

Multi-Modal; Prediction; Ensemble; Disease; Model Performance; Ensemble

Abstract

With the aid of Artificial Intelligence (AI), information technology has enhanced the illness diagnosis and prognosis in the overall healthcare processes by accelerating clinical decision-making. This research work presents a new concept, a multi-modal learning approach, for the diagnosis of important medical conditions like lung diseases, diabetes, heart diseases, and kidney diseases. This tweaking method produces a hybrid model by incorporating the potential of XGBoost for prediction with a mixture of logistic regression for understanding. This approach of analysis enables a comprehensive analysis of numerous patient factors incorporated in a clinical case. The proposed ensemble model is superior to the individual models and proves its ability to identify various diseases with an accuracy of 94%. These findings show that ensemble learning eradicates the chances of enhancing diagnostic and prognostic classification accuracy in real-world practical areas, and it fills the existing gap of escalating the precision of diagnosing tools prevalent in the healthcare industry, thereby laying a strong foundation for the application of multi-stream intelligent systems.

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  - Yogesh Tarachand Patil
AU  - Pallavi Soni
AU  - Vivek Verma
AU  - Deepak Bishnoi
PY  - 2025
DA  - 2025/04/19
TI  - Leveraging Artificial Intelligence for Multi-Modal Learning in Healthcare Applications
BT  - Proceedings of the International Conference on Advancements in Computing Technologies and Artificial Intelligence (COMPUTATIA-2025)
PB  - Atlantis Press
SP  - 308
EP  - 317
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-700-7_25
DO  - 10.2991/978-94-6463-700-7_25
ID  - Patil2025
ER  -