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Categorization of self care problem for children with disabilities using partial swarm optimization approach

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Abstract

Self-care or personal care denotes those actions or doing that a person undertakes in supporting personal health, limiting personal illness, preventing personal disease and reinstating their own health. Self-caring is big challenge in exceptional/disabled children. With recent advancement in artificial intelligence in last few years, machine learning can be used for classification of self-care problem in children with different age groups. The paper proposed an enhanced expert system based on machine learning for diagnose and classification of self-care issues in children with physical and mental disorder. Partitioned Multifilter with Partial Swarm Optimization (PM-PSO) is used for attribute/feature selection and the outcomes are analogize with Principal Component Analysis (PCA). The preferred features/attributes are tested, trained and validated on following classifiers:-Naïve Bayes, Multilayer Perception (MLP), C-4.5 and Random Tree. tenfolded cross validation is used for validation, testing and training. PCA selects 32 attributes and shows truly categorized instances i.e. accuracy as: (1) 80% for Naïve Bayes; (2) 68.57% for MLP; (3) 68.57% for C 4.5 and; (4) 64.28% for Random Tree. The classifiers show a significant improvement in performance with PM-PSO feature selector. 50 attributes were selected with PM-PSO. It shows truly categorized instances/accuracy as: (1) 81% for Naïve Bayes; (2) 80% for MLP; (3) 80% for C 4.5 and; (4) 78.57% for Random tree.

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Acknowledgement

The author recognises Zarchi et al. [26] for donating SCADI dataset to UCI for research purpose.

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  1. Department of Electronics and Communication Engineering, Giani Zail Singh Campus College of Engineering and Technology, MRSPTU, Bathinda, India

    Manoj Sharma

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  1. Manoj Sharma
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Correspondence to Manoj Sharma.

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Sharma, M. Categorization of self care problem for children with disabilities using partial swarm optimization approach. Int. j. inf. tecnol. 14, 1835–1843 (2022). https://doi.org/10.1007/s41870-020-00426-8

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  • DOI: https://doi.org/10.1007/s41870-020-00426-8

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