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.

Secure authentication and accurate localization among Internet of Things (IoT) sensors are pivotal for the functionality and integrity of IoT networks. IoT authentication and localization are intricate and symbiotic, impacting both the security and operational functionality of IoT systems. Hence, accurate localization and lightweight authentication on resource-constrained IoT devices pose several challenges. To overcome these challenges, recent approaches have used encryption techniques with well-known key infrastructures. However, these methods are inefficient due to the increasing number of data breaches in their localization approaches. This proposed research efficiently integrates authentication and localization processes in such a way that they complement each other without compromising on security or accuracy. The proposed framework aims to detect active attacks within IoT networks, precisely localize malicious IoT devices participating in these attacks, and establish dynamic implicit authentication mechanisms. This integrated framework proposes a Correlation Composition Awareness (CCA) model, which explores innovative approaches to device correlations, enhancing the accuracy of attack detection and localization. Additionally, this framework introduces the Pair Collaborative Localization (PCL) technique, facilitating precise identification of the exact locations of malicious IoT devices. To address device authentication, a Behavior and Performance Measurement (BPM) scheme is developed, ensuring that only trusted devices gain access to the network. This work has been evaluated across various environments and compared against existing models. The results prove that the proposed methodology attains 96% attack detection accuracy, 84% localization accuracy, and 98% device authentication accuracy.