Research on Infant Health Diagnosis and Intelligence Development Based on Machine Learning and Health Information
Siyu Wang1, Min Li1, Soo Boon Ng2
1Teachers College, Chengdu University, Chengdu, China.
Insights
This study uses machine learning and health statistics for infant health diagnosis, focusing on early brain development for better physical and mental health outcomes. Machine learning offers superior data analysis for predicting developmental trajectories.
Area of Science:
- Pediatric Health Informatics
- Developmental Psychology
- Computational Intelligence
Background:
- Childhood intelligence is linked to brain development, particularly in early stages.
- Promoting healthy development in young children is crucial for their future well-being.
- Existing statistical methods have limitations in analyzing complex health data.
Purpose of the Study:
- To develop an intelligent health diagnosis system for infants using machine learning.
- To analyze and model infant health, intelligence, and physical/mental development.
- To leverage big data for improved pediatric health research.
Main Methods:
- Data preprocessing and feature screening using machine learning and health statistics.
- Application of machine learning theories for data analysis and mining.
- Development of a health state model for intuitive visualization of infant development.
Main Results:
- Machine learning methods outperform traditional statistics in extracting hidden information from large datasets.
- The developed model provides data-driven insights into infant physical and mental health.
- Enhanced learning and generalization capabilities were observed with ML approaches.
Conclusions:
- Intelligent health diagnosis systems are vital for monitoring and promoting child development.
- Machine learning and big data analytics offer powerful tools for pediatric health research.
- Early brain development analysis is key to improving infant intelligence and overall 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.
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