Models and Algorithms for the Diagnosis of Parkinson's Disease and their Realization on the Internet of Things Network

Abstract

This article aims to investigate an innovative approach utilizing model, algorithms and IoT technology for early Parkinson's disease detection. It introduces the comprehensive IoT network that has the IoT platform, enabling the collection of voice data via mobile phones, extraction of relevant features and data processing. Within this process, a Fully Connected Neural Network (FCNN) model is employed to calculate the probability of Parkinson's disease, potentially providing healthcare professionals and patients with a convenient, accurate, and early diagnostic tool. The study delves into the structure, algorithms, and the integral role of the FCNN within the IoT network, emphasizing its potential impact on the healthcare sector.