A Deep Auto-Optimized Collaborative Learning (DACL) model for disease prognosis using AI-IoMT systems

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: May 4, 2024

Abstract In modern healthcare, integrating Artificial Intelligence (AI) and Internet of Medical Things (IoMT) is highly beneficial has made it possible to effectively control disease using networks interconnected sensors worn by individuals. The purpose this work develop an AI-IoMT framework for identifying several chronic diseases form the patients’ medical record. For that, Deep Auto-Optimized Collaborative Learning (DACL) Model, a brand-new framework, been developed rapid diagnosis like heart disease, diabetes, stroke. Then, Auto-Encoder Model (DAEM) used in proposed formulate imputed preprocessed data determining fields characteristics or information that are lacking. To speed up classification training testing, Golden Flower Search (GFS) approach then utilized choose best features from data. addition, cutting-edge Bias Integrated GAN (ColBGaN) model created precisely recognizing classifying types records patients. loss function optimally estimated during Water Drop Optimization (WDO) technique, reducing classifier’s error rate. Using some well-known benchmarking datasets performance measures, DACL’s effectiveness efficiency evaluated compared.

Language: Английский

Medical Image Cryptography Using Chaotic Methods: an study

Published: Feb. 16, 2025

Language: Английский