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

Area of Science:

• Gerontology
• Public Health
• Artificial Intelligence

Background:

• Investigates chronic diseases in Henan's older population, highlighting service gaps.
• Analyzes rehabilitation needs versus current service supply in medical and elderly care.
• Explores root causes of diverse needs and insufficient professional medical and daily care.

Purpose of the Study:

• To propose an intelligent prediction system for chronic diseases using big data and deep learning (DL).
• To enhance the delivery of high-quality medical resources and grassroots services for chronic disease patients.
• To enable long-term community-based care for the elderly with chronic conditions.

Main Methods:

• Utilized big data and deep learning (DL) in the sports domain for disease prediction.
• Developed methods for sinking high-quality medical resources to community levels.
• Focused on improving training, guidance, and assistance measures for grassroots services.

Main Results:

• Proposed a novel intelligent prediction system for chronic diseases.
• Identified strategies to improve the supply and accessibility of rehabilitation services.
• Demonstrated potential for enhancing community-based care for chronically ill populations.

Conclusions:

• The intelligent system can improve the sinking of medical resources and grassroots services.
• Supports chronically ill elderly to remain in community settings with accessible care.
• Aids in developing regional medical rehabilitation systems and long-term care policies.