Data Set Construction Method for Intelligent Health Care and Its Application

Abstract

The rapid development of Internet and computer technology makes it possible to improve smart health care services in today’s aging population. However, there are some data problems that seriously restrict the process of intelligence in the field of elderly care, such as the lack of real data, the interference of dirty data, and too few standard samples. To solve the problem of lacking data set, this paper proposes a three-stage data set construction method based on machine learning on the basis of small sample data which are collected from the community health care in a city. In the first stage, this paper designs a tree structure-based generation strategy to generate the basic attributes of the data set according to the distribution of the original data. In the second stage, this paper obtains the basic behavioral ability evaluation index of the samples with naive Bayesian algorithm. In the third stage, this paper constructs a variety of multiple linear regression equations to get high-order behavioral ability index and evaluation stage on the basis of the first two stages. In order to verify the effectiveness of the data set generated by the model for downstream tasks, this paper designs multiple rehabilitation training plan recommendation models based on the generated data with neural network, and achieves 5 multi-classification tasks and 2 multi-label classification tasks. This paper verifies the authenticity and validity of generated data through analysis of experimental results and expert knowledge.

Keywords

• |smart health care service|small sample data|naive bayes|multiple linear regression