Classification of Child Disability Using Artificial Neural Network

Abstract

There are millions of children suffering from disability across the globe. Due to its implicit characteristics, the classification and diagnosis of children with disability has long been a difficult issue. This paper proposes a systematic approach for classification of potential cases more accurately and easily by use of Artificial Neural Network (ANN). In this paper, an attempt has been made to apply Artificial Neural Networks (ANN) for classification of disability in children based on the combination of symptoms. ANNs are nonparametric statistical tools designed using bio-inspired approaches. ANNs have been configured for application in different fields as diverse as engineering technology to financial forecasting. The primary inspiration of the working principle of the ANN is created by mimicking the functioning of the human brain. ANNs can learn input patterns and use this knowledge to make predictions. ANNs are known to be adaptive, robust and can learn process data irrespective of methods of creation. All these spin-offs can be associated with this approach of predicting child disabilities using ANN. Such an approach has been applied in this paper to classify the type of disability and relating them to the symptoms observed at birth of a child along with mother’s health condition during pregnancy. The advantage of such a system is derived from the strengths of the ANN. The paper proposes a Multi Layer Perceptron (MLP)-based ANN model for classifying types of disability among children. The model comprises of a single input layer which correspond to different symptoms at birth of a baby and maternal health during pregnancy and outputs corresponding to different types of disability. The preliminary results obtained using test data are satisfactory and shows that the system can be used as an effective tool to classify children with disability and obtaining adequate information before consulting a specialist.