Identification of self-care problem in children using machine learning
Identification of self-care problems in children is a challenging task for medical professionals owing to its complexity and time consumption. Furthermore, the shortage of occupational therapists worldwide makes the task more challenging. Machine learning methods have come to the aid of reducing the...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-03-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024030081 |
| _version_ | 1797259784564506624 |
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| author | Maya John Hadil Shaiba |
| author_facet | Maya John Hadil Shaiba |
| author_sort | Maya John |
| collection | DOAJ |
| description | Identification of self-care problems in children is a challenging task for medical professionals owing to its complexity and time consumption. Furthermore, the shortage of occupational therapists worldwide makes the task more challenging. Machine learning methods have come to the aid of reducing the complexity associated with problems in diverse fields. This paper employs machine learning based models to identify whether a child suffers from self-care problems using SCADI dataset. The dataset exhibited high dimensionality and imbalance. Initially, the dataset was converted into lower dimensionality. Imbalanced dataset is likely to affect the performance of machine learning models. To address this issue, SMOTE oversampling method was used to reduce the wide variations in the class distribution. The classification methods used were Naïve bayes, J48 and random forest. Random forest classifier which was operated on SMOTE balanced data obtained the best classification performance with balanced accuracy of 99%. The classification model outperformed the existing expert systems. |
| first_indexed | 2024-03-07T14:01:00Z |
| format | Article |
| id | doaj.art-565f9fdda4d34202ada3f50cdfb2c5fe |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-04-24T23:14:56Z |
| publishDate | 2024-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-565f9fdda4d34202ada3f50cdfb2c5fe2024-03-17T07:56:57ZengElsevierHeliyon2405-84402024-03-01105e26977Identification of self-care problem in children using machine learningMaya John0Hadil Shaiba1Artificial Intelligence and Data Analytics (AIDA) Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia; Corresponding author.Department of Computer Science, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaIdentification of self-care problems in children is a challenging task for medical professionals owing to its complexity and time consumption. Furthermore, the shortage of occupational therapists worldwide makes the task more challenging. Machine learning methods have come to the aid of reducing the complexity associated with problems in diverse fields. This paper employs machine learning based models to identify whether a child suffers from self-care problems using SCADI dataset. The dataset exhibited high dimensionality and imbalance. Initially, the dataset was converted into lower dimensionality. Imbalanced dataset is likely to affect the performance of machine learning models. To address this issue, SMOTE oversampling method was used to reduce the wide variations in the class distribution. The classification methods used were Naïve bayes, J48 and random forest. Random forest classifier which was operated on SMOTE balanced data obtained the best classification performance with balanced accuracy of 99%. The classification model outperformed the existing expert systems.http://www.sciencedirect.com/science/article/pii/S2405844024030081Self-care problemmachine learningImbalanced dataClassification |
| spellingShingle | Maya John Hadil Shaiba Identification of self-care problem in children using machine learning Heliyon Self-care problem machine learning Imbalanced data Classification |
| title | Identification of self-care problem in children using machine learning |
| title_full | Identification of self-care problem in children using machine learning |
| title_fullStr | Identification of self-care problem in children using machine learning |
| title_full_unstemmed | Identification of self-care problem in children using machine learning |
| title_short | Identification of self-care problem in children using machine learning |
| title_sort | identification of self care problem in children using machine learning |
| topic | Self-care problem machine learning Imbalanced data Classification |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024030081 |
| work_keys_str_mv | AT mayajohn identificationofselfcareprobleminchildrenusingmachinelearning AT hadilshaiba identificationofselfcareprobleminchildrenusingmachinelearning |