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. 2022 Jun 2:10:846598.
doi: 10.3389/fpubh.2022.846598. eCollection 2022.

Research on Infant Health Diagnosis and Intelligence Development Based on Machine Learning and Health Information Statistics

Siyu Wang  1 , Min Li  1 , Soo Boon Ng  2
Affiliations

Research on Infant Health Diagnosis and Intelligence Development Based on Machine Learning and Health Information Statistics

Siyu Wang et al. Front Public Health. .

Abstract

Intelligent health diagnosis for young children aims at maintaining and promoting the healthy development of young children, aiming to make young children have a healthy state and provide a better future for their physical and mental health development. The biological basis of intelligence is the structure and function of human brain and the key to improve the intelligence level of infants is to improve the quality of brain development, especially the early development of brain. Based on machine learning and health information statistics, this paper studies the development of infant health diagnosis and intelligence, physical and mental health. Pre-process the sample data, and use the filtering method based on machine learning and health information statistics for feature screening. Compared with traditional statistical methods, machine learning and health information statistical methods can better obtain the hidden information in the big data of children's physical and mental health development, and have better learning ability and generalization ability. The machine learning theory is used to analyze and mine the infant's health diagnosis and intelligence development, establish a health state model, and intuitively show people the health status of their infant's physical and mental health development by means of data. Moreover, the accumulation of these big data is very important in the field of medical and health research driven by big data.

Keywords: big data; health information statistics; infant health diagnosis; intelligence development; machine learning.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Schematic diagram of the structure of infant intelligent health diagnosis and intelligence development.
Figure 2
Figure 2
Training flow chart of logistic regression algorithm model.
Figure 3
Figure 3
Health information statistics of logistic regression combined data and basic data.
Figure 4
Figure 4
Health information statistics of logistic regression combined data and basic data.
Figure 5
Figure 5
Curve representations of three kernel functions of machine learning basic data and health information statistics.
Figure 6
Figure 6
Curve representations of three kernel functions of machine learning basic data and health information statistics.
Figure 7
Figure 7
Curve representation of three kernel functions of machine learning basic data and health information statistics.
Figure 8
Figure 8
The ROC curve representation of three kernel functions under machine learning combined data.
Figure 9
Figure 9
The ROC curve representation of three kernel functions under machine learning combined data.
Figure 10
Figure 10
ROC curve performance of three kernel functions under machine learning combined data.
See this image and copyright information in PMC

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