Prediction of Child Malnutrition using Machine Learning

Sometimes malnourished children fall into some serious health issues. And doctors are unable to find out the root causes of their illness, but they used to apply some practices which were not appropriate for every child. Children often die because of this reason. So, it is very dangerous for malnourished children. Along these lines, the fundamental point of our review is to anticipate hunger status of a 1 to 5 years more established kid in Asia by utilizing AI. Looked for ongoing examination papers (2010 - 2020) which identified our point and combined outcomes into a synopsis of what is and isn't known and tried to find out advantages and drawbacks. As explained in the introduction part, to do so, selected a suitable dataset from open source. And they went through many articles to know about Machine Learning Algorithms like their advantages and drawbacks. So, four widely used Machine Learning classifiers like Random Forest, Support Vector Machine, K-Nearest Neighbors, Logistic Regression have been considered to predict a good accuracy score of malnutrition status among under 5 years old. At last, they looked for the best algorithms according to their accuracy score. Based on various performances of Machine learning Algorithms, the best results were performed with Random Forest and Logistic Regression, which demonstrate an accuracy of 91.11 % and 89.88 %, Train accuracy of 1.000 and 0.847. Additionally, a most extreme discriminative ability appeared by Random Forest classification. Here they analyzed those 4 ML algorithms to find which one is performing best. Among them Random Forest and Logistic Regression performing very well. And in future they will do some beneficial work for malnourished children.