A Cognitive Medical Decision Support System for IoT-Based Human-Computer Interface in Pervasive Computing Environment

Gou, Haosong; Zhang, Gaoyi; Medeiros, Elias Paulino; Jagatheesaperumal, Senthil Kumar; de Albuquerque, Victor Hugo C.

doi:10.1007/s12559-023-10242-4

Cited by 4 publications

(1 citation statement)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The suggested method is quite sophisticated in design, though, and takes more time to predict disease outcomes. Gou et al 35 developed a new and flexible cognitive medical decision system for strengthening the end-user confidence with proper training and computational performance. Zhang et al 36 introduced a physics guided deep learning algorithm for identifying and classifying coronary disease by adopting IoT technology.…”

Section: Related Workmentioning

confidence: 99%

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

Nandagopal,

Seerangan,

Govindaraju

et al. 2024

Sci Rep

View full text Add to dashboard Cite

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

show abstract

Section: Related Workmentioning

confidence: 99%