Intelligent Prediction and Optimization Algorithm for Chronic Disease Rehabilitation in Sports Using Big Data.

Xuelei Zhang,Xiaofeng Wang

doi:10.1155/2021/9920421

Abstract

This paper investigates chronic diseases in the older population in the Chinese province of Henan and analyzes the rehabilitation needs and the current supply of related services in different levels of medical and elderly care institutions. We explore the fundamental causes for the diversified needs and insufficient supply of chronic disease patients in professional medical services and daily care. Using big data and deep learning (DL) in the sports domain, we propose a novel and intelligent prediction system for chronic diseases. Our model explores effective sinking methods of high-quality medical resources, training and guidance practices, assistance and guidance measures, and the ability to improve the grassroots services so that more chronically ill populations can stay in the community family as long as possible. In such an environment, they can receive cheap, safe, and suitable services. It can also lead to further improvement in constructing the government's regional medical rehabilitation care service system and can formulate long-term care relevant compensation policies.

Highlights

Introduction e UnitedNations “Global Population Development Report” shows that, as of June 2015, the global population reached 7.3 billion. e report claims that about 12% are aged 60 and above, and it is growing at an annual rate of 3.26%
(1) Inclusion criteria: (a) age ≥60 years; (b) all chronic diseases have been diagnosed by medical institutions at the second level and above and meet the diagnostic criteria for various chronic diseases; (c) consciousness, normal cognitive function, able to communicate and effectively cooperate; and (d) those who give informed consent and voluntarily participated in the research
We use stochastic gradient descent (SGD) to minimize the log loss when training the model from scratch. e training dataset uses the ILSVRC2015-VID dataset, which contains more than 30 basic categories and 4500 target image sequences for training. e initial value is set using Gaussian distribution, and the scale is set according to the improved Xavier method. e initial learning rate of the convolutional layer is set to 0.001. e training process includes 50 iterations, each iteration includes 5000 sample pairs, and the learning rate becomes 0.87 every 50 epochs

Summary

Introduction

Introduction e UnitedNations “Global Population Development Report” shows that, as of June 2015, the global population reached 7.3 billion. e report claims that about 12% are aged 60 and above, and it is growing at an annual rate of 3.26%. E neural network [10,11,12,13,14] model is composed of many neurons. Inclusion and Exclusion Criteria for the Elderly with Chronic Diseases

Results

Conclusion