# Care2Vec: A Deep learning approach for the classification of self-care problems in physically disabled children

## TL;DR

Care2Vec is a novel deep learning approach combining autoencoders and neural networks to improve the classification accuracy of self-care problems in children with physical disabilities, aiding healthcare diagnosis.

## Contribution

This paper introduces Care2Vec, a new deep learning method that outperforms traditional models in classifying self-care issues in disabled children using WHO-based data.

## Key findings

- Care2Vec achieves higher accuracy than decision trees and traditional neural networks.
- The approach effectively combines unsupervised and supervised learning.
- It demonstrates potential for aiding healthcare professionals in diagnosis.

## Abstract

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.