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

作     者：Moshe Dayan Sirapangi S.Gopikrishnan 

作者机构：School of Computer Science and EngineeringVIT-AP UniversityAmaravathiAndhra Pradesh522241India 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2024年第79卷第5期

页      面：2229-2251页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：We acknowledge the support for the evaluation of the proposed model from Intel IoT Center for Excellence  VIT-AP University and VIT-AP Health Center  VIT-AP University 

主　　题：Predictive health modeling Medical Internet of Things explainable artificial intelligence personalized feature selection preemptive analysis 

摘      要：Medical Internet of Things(IoT)devices are becoming more and more common in *** 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 *** 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 *** is a multimodal approach integrating pre-emptive analysis,personalized feature selection,and explainable AI for real-time health monitoring and disease *** using AI for early disease detection,personalized health recommendations,and transparency,healthcare will be *** 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 *** metrics show the model’s superiority in real-time health *** 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 *** 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 *** research shows that this innovative approach improves patient outcomes and healthcare efficiency in the real world.