Using classification techniques for statistical analysis of Anemia.

Abstract

Anemia in children is becoming a worldwide problem owing to the unawareness among people regarding the disease, its causes and preventive measures. This study develops a decision support system using data mining techniques that are applied to a database containing data about nutritional factors for children. The data set was taken from NFHS-4, a survey conducted by the Government of India in 2015-16. The work attempts to predict anemia among children and establish a relation between mother's health and diet during pregnancy and its effects on anemic status of her child. It aims to help parents and clinicians to understand the influence of an infant's feeding practices and diet on his/her health and provide guidelines regarding diet to prevent anemia. Earlier, systems were built on computer using medical experts' advicewhich was then translated into algorithms for use. However, this method was time consuming thus, artificial intelligence came into play utilizing knowledge discovery and data mining tools for predictive modeling. The two techniques, decision tree and association rule mining has been applied and compared to select more appropriate technique for this particular task and a model is proposed in the healthcare domain with the aim to reduce the risk of the blood-related disease anemia.