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MAIPFE: An Efficient Multimodal Approach Integrating Pre-Emptive Analysis, Personalized Feature Selection, and Explainable AI

Moshe Dayan Sirapangi1, S. Gopikrishnan1,*

1 School of Computer Science and Engineering, VIT-AP University, Amaravathi, Andhra Pradesh, 522241, India

* Corresponding Author: S. Gopikrishnan. Email: email

Computers, Materials & Continua 2024, 79(2), 2229-2251. https://doi.org/10.32604/cmc.2024.047438

Abstract

Medical Internet of Things (IoT) devices are becoming more and more common in healthcare. This has created a huge need for advanced predictive health modeling strategies that can make good use of the growing amount of multimodal data to find potential health risks early and help individuals in a personalized way. Existing methods, while useful, have limitations in predictive accuracy, delay, personalization, and user interpretability, requiring a more comprehensive and efficient approach to harness modern medical IoT devices. MAIPFE is a multimodal approach integrating pre-emptive analysis, personalized feature selection, and explainable AI for real-time health monitoring and disease detection. By using AI for early disease detection, personalized health recommendations, and transparency, healthcare will be transformed. The Multimodal Approach Integrating Pre-emptive Analysis, Personalized Feature Selection, and Explainable AI (MAIPFE) framework, which combines Firefly Optimizer, Recurrent Neural Network (RNN), Fuzzy C Means (FCM), and Explainable AI, improves disease detection precision over existing methods. Comprehensive metrics show the model’s superiority in real-time health analysis. The proposed framework outperformed existing models by 8.3% in disease detection classification precision, 8.5% in accuracy, 5.5% in recall, 2.9% in specificity, 4.5% in AUC (Area Under the Curve), and 4.9% in delay reduction. Disease prediction precision increased by 4.5%, accuracy by 3.9%, recall by 2.5%, specificity by 3.5%, AUC by 1.9%, and delay levels decreased by 9.4%. MAIPFE can revolutionize healthcare with preemptive analysis, personalized health insights, and actionable recommendations. The research shows that this innovative approach improves patient outcomes and healthcare efficiency in the real world.

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Cite This Article

APA Style
Sirapangi, M.D., Gopikrishnan, S. (2024). MAIPFE: an efficient multimodal approach integrating pre-emptive analysis, personalized feature selection, and explainable AI. Computers, Materials & Continua, 79(2), 2229-2251. https://doi.org/10.32604/cmc.2024.047438
Vancouver Style
Sirapangi MD, Gopikrishnan S. MAIPFE: an efficient multimodal approach integrating pre-emptive analysis, personalized feature selection, and explainable AI. Comput Mater Contin. 2024;79(2):2229-2251 https://doi.org/10.32604/cmc.2024.047438
IEEE Style
M.D. Sirapangi and S. Gopikrishnan, "MAIPFE: An Efficient Multimodal Approach Integrating Pre-Emptive Analysis, Personalized Feature Selection, and Explainable AI," Comput. Mater. Contin., vol. 79, no. 2, pp. 2229-2251. 2024. https://doi.org/10.32604/cmc.2024.047438



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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