OPTIMIZING DISEASE DIAGNOSIS THROUGH MULTIMODAL PATIENT DATA ANALYTICS IN THE INTERNET OF HEALTHCARE THINGS (IoHT)
Year: 2025
Type: article
Abstract: This article introduces a Multi-Modal Patient Data Analytics Model (MMPDAM) to improve wearable sensor input validation in the Internet of Healthcare Things (IoHT). This model is designed to improve error detection in different wearable sensor data acquired sequences to augment disease diagnosis through maximum clean data extraction. This model is powered by the deep neural network learning paradigm. This paradigm is used for error detection through sequence validation and data extraction throughout the continuous and discontinuous sequences obtained from the wearable sensors. The proposed model achieves 10.329% high data analysis, 10.88% high error detection, and 10.47% high data extraction for the maximum observation time.
Authors: Ezhumalai Perumal, B.B. Abduganiev, F.P. Raxmonqulov
Language: English
Topic: Internet of Things and AI
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6893316625
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