Analysing the State of Machine Learning and Deep Learning for Child Malnutrition Prediction

Author(s): S Dhivya and Dr. TA Sangeetha

Abstract:

Malnutrition is the inadequacy in a person’s consumption of nutrients. Malnutrition is considered has pivotal health issue in day-to-day life. Causes of malnutrition include poor feeding practices, inadequate breastfeeding, inadequate nutritional knowledge, and diseases. The main problem of malnutrition is found in underdeveloped and developing countries. Most children suffering from malnutrition in mild to moderate forms are unnoticed in different parts of the world which affects their growth at early ages. Hence, it is important to detect malnutrition at an early stage to reduce further healthcare costs and help healthcare providers reduce the effects caused by malnutrition. In recent years, Machine learning (ML) and Deep Learning (DL) approaches have frequently used in healthcare domain which accurately predict disease in a short period so that patients can get diagnosis in minimal time. These models efficiently identify malnourished children will help to prevent the risk of death and can reduce physical and health issues by taking necessary measures or treatment. This work presents a detailed review of various ML and DL frameworks developed to predict the malnutrition status of children. Initially, different malnutrition prediction frameworks designed by many researchers based on ML and DL algorithms are studied in brief. Then, a comparative study is conducted to understand the drawbacks of those frameworks and suggest a new solution to predict malnutrition in children accurately.

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