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Elderly disability risk prediction method and system based on machine learning

A technology of risk prediction and machine learning, applied in neural learning methods, instruments, informatics, etc., can solve the problems of lack of risk prediction of loss of daily life ability, the elderly are limited to a specific disease, etc., to achieve convenient and accurate disability risk prediction , the effect of raising awareness of prevention

Pending Publication Date: 2021-04-06
ZHEJIANG UNIV
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Problems solved by technology

At present, the assessment of the physical health status of the elderly is generally limited to a specific disease, and there is a lack of effective methods for predicting the risk of losing the ability of daily life

Method used

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  • Elderly disability risk prediction method and system based on machine learning
  • Elderly disability risk prediction method and system based on machine learning

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Embodiment Construction

[0025] The present invention will be further described below in conjunction with accompanying drawing.

[0026] Disability, Disability Scale and Disability Risk are introduced first. In 2001, the World Health Organization defined disability in the "International Classification of Functions" as "an umbrella term for impairment, limitation of activities and limitation of social participation. Negative aspects of the interaction between factors, environment and personal factors". Generally, the disability level of the population is quantitatively assessed by the disability scale. Classic disability scales include EARRS, FHS, ADL, EDAS, etc. In one embodiment of the invention, the ADL scale is used. The ADL scale has 14 evaluation criteria, including two parts: one is the physical self-care scale, with 6 items in total: going to the toilet, eating, dressing, grooming, walking and bathing; the other is the instrumental daily living ability scale, There are 8 items in total: mak...

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Abstract

The invention discloses an elderly people disability risk prediction method and system based on machine learning, and belongs to the field of big data health condition analysis and prediction. The method comprises the following steps: acquiring basic data, social and economic data and medical history information data of the elderly to form a sample data set, and preprocessing the sample data set; performing feature extraction on the preprocessed sample data through methods such as a random forest model; establishing a multi-layer neural network through the extracted features, and performing training by using sample data to obtain an elderly population disability risk prediction model; and acquiring related data of a to-be-tested person, and inputting the related data into the risk prediction model to obtain a predicted risk value. According to the invention, the elderly disability risk can be conveniently and accurately predicted.

Description

technical field [0001] The invention belongs to the field of analysis and prediction of health status of big data, and in particular relates to a method and system for predicting the disability risk of the elderly based on machine learning. Background technique [0002] With the improvement of social living standards, population aging and acceleration of aging, the number of elderly people who have lost the ability of daily life, that is, the number of disabled elderly people is also increasing year by year. The daily life of the disabled elderly needs the care of others, and the use of external nursing services to make up for the lost physiological functions requires a lot of social care resources. Effectively predicting the disability risk of the elderly population in a specific area, on the one hand, can optimize resource allocation such as community care, pension institutions, social and commercial insurance; on the other hand, it can also help to intervene in the living...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16H50/30G06N3/08G06N3/04G06K9/62
CPCG16H50/30G06N3/08G06N3/045G06F18/24323
Inventor 吴超李皓
Owner ZHEJIANG UNIV
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