Abstract Malnutrition is regarded as a crucial health issue of a nation considering today's children as tomorrow's youngsters and leaders who directly influence the fruitful development of the nation. The exact amount of nutrition is the most required element for the survival, development, growth of the children in this society. The foremost goal of this proposed phenomenon in children less than five years is suffering from a lack of nutrition. The problem of Malnutrition and anemia is majorly found in under-developed and developing countries. To overcome this problem we make use of various Machine learning and data mining approaches to predict the malnutrition condition of a child less than five years based on the training data-sets. Training data-sets downloaded from www.kaagle.com.Various factors such as Gender, Age, HAZ, WAZ, etc…are extracted. Classification techniques used for malnutrition status prediction. We use algorithms such as "Bayesian classifier" and "K-nearest neighbor" for prediction. The results will be compared and an efficient algorithm will be identified. With the help of testing and Checking knowledge, precautionary measures will be led with the aid of medical practitioners to minimize the anemia and malnutrition condition in a child. We build this as a real-time software application useful for society. To build the real-time application we use technologies such as "Visual Studio" for the front end and "SQL server" for the back end. Both of these tools are powerful tools to work with the real-time application. This system specifies how the classification methods can classify anemia and malnutrition condition of children below five years of age. In General, our proposed method is designed in such a way that, the most accurate results are obtained to find the malnutrition and anemia status based on data sets collected.
Keywords Malnutrition, Anemia, KNN, Naive Bayes, Data mining, Classification rules
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DOI:
10.17148/IJARCCE.2021.10793
