Random Programming Model Analysis of Infant Development Impact Based on Random Forest Algorithm

Mothers' mental health status has a significant impact on infants' cognitive, emotional and social behaviour. In this paper, we first preprocess the data of infants' behavioural characteristics, and then use Spearman's coefficient to correlate the mother's characteristics with the infant's characteristics. Next, the trained SVM and RF are compared, and finally RF is chosen to classify infants whose types are not yet clear and calculate the importance of the features, and it is found that EPDS has a stronger relationship with the behavioural characteristics of infants. Then a planning model is built to calculate at least how much it needs to be spent to make the infant behavioural characteristics transformed. Finally, TOPSIS based on combined weights is established to make a comprehensive judgement on the sleep quality of infants, which is classified into four categories using K-means. It is concluded that it takes at least ∃65,785.3 to change the infant's behavioural characteristics from ambivalent to moderate, and CBTS is the main treatment; it takes at least ∃65,610 to change the infant's behavioural characteristics from ambivalent to quiet, and EPDS is the main treatment.