Recommended System for Controlling Malnutrition in Iranian Children 6 to 12 Years Old using Machine Learning Algorithms

Document Type : Original Article


1 M.Sc. in Information Technology Engineering, Ghiaseddin Jamshid Kashani University, Abyek, Iran

2 Faculty Member of Computer Department, Faculty of Computer, Eyvanekey Non-Profit University, Semnan


Iran is facing low levels of all three types of children's nutrition like nutrient and micronutrients deficiency and overeating. The most common nutritional problems and child deaths are vitamin deficiencies and food quality. The purpose of this research is to plan food recommended system to control malnutrition in children 6 to 12 years old using hybrid machine learning algorithms.  The results of this research are applicable in terms of target research. In terms of the implementation method, it is a descriptive survey and the process of gathering information is quantitative data. The dataset used includes 1001 data points collected from the health centers of Mianeh city located in East Azerbaijan in Iran from the integrated apple web system. In this research, the Python programming language has been used to analyze the child nutrition dataset, and AdaBoost and Decision Tree hybrid algorithms have been utilized for the child nutrients recommender system. We concluded that the number of meal features using the Decision Tree algorithm with 98.5% accuracy was more important than other nutritional features of children in recognizing malnutrition in them. From a review of 1001 data into the child nutrition dataset, 807 children are underweight and malnourished, 170 children are normal weight, 20 children are obese and four children are overweight. Therefore, the high exactness of hybrid algorithms in these studies has been able to have a high alignment with the opinion of nutritionists from 2019 to 2020.


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