Accurate classification of self-care problems in children who suffer from physical and motor affliction is an important problem in the healthcare industry. This is a difficult and a time consumming process and it needs the expertise of occupational therapists. In recent years, healthcare professionals have opened up to the idea of using expert systems and artificial intelligence in the diagnosis and classification of self care problems. In this study, we propose a new deep learning based approach named Care2Vec for solving these kind of problems and use a real world self care activities dataset that is based on a conceptual framework designed by the World Health Organization (WHO). Care2Vec is a mix of unsupervised and supervised learning where we use Autoencoders and Deep neural networks as a two step modeling process. We found that Care2Vec has a better prediction accuracy than some of the traditional methods reported in the literature for solving the self care classification problem viz. Decision trees and Artificial neural networks.
@cite_1: Application of artificial intelligence to the problems of education is a relatively recent endeavor. This article will focus on one of the most promising aspects of artificial intelligence - expert systems technology - and some of the characteristics that make expert systems "intelligent". Selected present and poten- tial applications of expert systems to the field.of learning disabilities are presented along with examples of specific expert systems.
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@cite_2: Data mining is the useful tool to discovering the knowledge from large data. Different methods & algorithms are available in data mining. Classification is most common method used for finding the mine rule from the large database. Decision tree method generally used for the Classification, because it is the simple hierarchical structure for the user understanding & decision making. Various data mining algorithms available for classification based on Artificial Neural Network, Nearest Neighbour Rule & Baysen classifiers but decision tree mining is simple one. ID3 and C4.5 algorithms have been introduced by J.R Quinlan which produce reasonable decision trees. The objective of this paper is to present these algorithms. At first we present the classical algorithm that is ID3, then highlights of this study we will discuss in more detail C4.5 this one is a natural extension of the ID3 algorithm. And we will make a comparison between these two algorithms and others algorithms such as C5.0 and CART.
There have been many works in the literature on applications of machine learning and expert systems for classification and identification of disabilities. @cite_2 worked on identification of students with learning disabilities that includes dyslexia, attention deficit disorder and more using ANN. They recommend their model as "second opinion" to experts who in charge of learning disabilities evaluation. Hofmeister and Lubke @cite_1 and @cite_3 are some other works on applications of Artificial intelligence (AI) for diagnosis and classification of learning disabilities.