Application of Machine Learning in Determining the Classification of Children's Nutrition With Decision Tree

Abstract

The problem of nutrition for children is a health problem that must be solved by the government. Malnutrition is a very important problem in the development of children, especially during the growth period. Lack of nutritional intake in children will have a negative impact on resistance to the virus. This will risk death caused by malnutrition. There is direct monitoring from the government, hospitals, and health offices in looking at the classification of nutrition in children in a system. This study aims to classify the nutritional status of children using a machine learning model, which then the final result can show the classification of nutritional vulnerability in each patient at the North Aceh Hospital. The stages of the research include the identification of theories about nutritional problems. Second, data collection is in the form of symptoms and diagnosis of disease classification in machine learning implementation. The third is to analyze the data using the research and development (R&D) method according to the classification of children's nutrition. Finally, the implementation of the patient classification model with decision tree into machine learning The variables included in the system include JK(X1), U (X2), BB(X3), TB (X4), and BB (X3), which are the variables that have the most influence on malnutrition in children. The results of this study for testing weight 16, height 9.7, age 33 months, nutritional value 54,23772 which the program output results are normal. Patient Syafira Nisman, weight 9, height 72, age 21 months, suffered from malnutrition. The results of the research on the application of machine learning for the classification of malnutrition using the decision tree method make it easier for patients and hospitals to classify children's nutrition.

Penerapan Machine Learning Clustering K-Means dan Linear Regression Dalam Penentuan Tingkat Resiko Tuberkulosis Paru

“…Selanjutnya model K-means dapat terbagi dalam satu atau lebih cluster/grup. Metode ini membagi data menjadi cluster atau kelompok, mengelompokkan data dengan karakteristik yang sama ke dalam cluster yang sama, dan mengelompokkan data dengan karakteristik yang berbeda kedalam kelompok lain [16], [17].…”

“…Metode regresi linier merupakan metode yang terdiri dari satu atau lebih variabel independen yang biasa dengan notasi X dan satu variabel respon yang bisa diwakili dengan Y [20]. Regresi linier digunakan untuk dapat nilai variable peramalan dengan satu buah variabel yang terkait dengan nilai variabel lainnya dan dapat melakukan nilai prediksi lebih baik [6].…”

Application of Machine Learning in Predicting Children's Nutritional Status With Multiple Linear Regression Models

“…Specific and targeted interventions are needed to address this and should include efforts to prevent low birth weight and reduce health inequalities and require clustering for each region. [19]. Clinical control and growth monitoring should also be encouraged.…”

Machine Learning Method to Predict the Toddlers’ Nutritional Status

“…Several ML methods are used to classify/predict malnutrition or nutritional status in toddlers, including the naïve Bayes (NB) method [3][4][5][6], logistic regression [7], k-nearest neighbor (kNN) [4,5,8], decision tree (DT) [6,[9][10][11], support vector machine (SVM) [12], and learning vector quantization [13]. Several studies compare several ML methods to classify malnutrition in toddlers [4,[14][15][16][17][18][19].…”