The purpose of this study was to investigate the effects of mothers' physical and mental health on infant development. Through mathematical modeling and data analysis, correlations were found between mothers' physical and psychological indicators and infants' behavioral characteristics and sleep quality. Correspondence analysis and statistical methods were used to determine the degree of correlation between the two. The relationship model between maternal indicators and infant behavioral characteristics was established, and the decision tree model was used to predict infant behavioral characteristics. Through integer optimization problems and genetic algorithms, methods were proposed to adjust the psychological indicators of mothers to improve infant behavioral characteristics and minimize treatment costs. The results of the study provide a scientific basis for developing intervention strategies and promoting the healthy growth of infants.

Weiyi Zhang. A Study on the Influence of Mothers' Physical and Mental Indicators on Infants' Behavioral Characteristics and Sleep Quality Based on Decision Tree and Genetic Algorithm. Academic Journal of Computing & Information Science (2024), Vol. 7, Issue 5: 222-228. https://doi.org/10.25236/AJCIS.2024.070529.

[1] Newman L, Judd F, Olsson C A, et al. Early origins of mental disorder-risk factors in the perinatal and infant period [J]. BMC psychiatry, 2016, 16: 1-8.

[2] Eisenhower A S, Baker B L, Blacher J. Children's delayed development and behavior problems: Impact on mothers' perceived physical health across early childhood[J]. Social Science & Medicine, 2009, 68(1): 89-99.

[3] Jiang J, Zhang X, Yuan Z. Feature selection for classification with Spearman’s rank correlation coefficient-based self-information in divergence-based fuzzy rough sets[J]. Expert Systems with Applications, 2024: 123633.

[4] Montirosso R, Riccardi B, Molteni E, et al. Infant's emotional variability associated to interactive stressful situation: A novel analysis approach with Sample Entropy and Lempel–Ziv Complexity[J]. Infant Behavior and Development, 2010, 33(3): 346-356.

[5] Fletcher S, Islam M Z. Decision tree classification with differential privacy: A survey[J]. ACM Computing Surveys (CSUR), 2019, 52(4): 1-33.

[6] Back A D, Angus D, Wiles J. Determining the number of samples required to estimate entropy in natural sequences [J]. IEEE Transactions on Information Theory, 2019, 65(7): 4345-4352.

[7] Verma V K, Kumar B. Genetic algorithm: an overview and its application[J]. International Journal of Advanced Studies in Computers, Science and Engineering, 2014, 3(2): 21.