Implications of machine learning techniques for prediction of motor health disorders in Saudi Arabia
People with disabilities are cared for in Saudi Arabia in a way that guarantees their rights and improves the services they receive by giving them the resources they need for prevention, care, and rehabilitation. Through various medical, psychological, social, educational, media, and regulatory mean...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-08-01
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| Series: | Alexandria Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016825008385 |
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| Summary: | People with disabilities are cared for in Saudi Arabia in a way that guarantees their rights and improves the services they receive by giving them the resources they need for prevention, care, and rehabilitation. Through various medical, psychological, social, educational, media, and regulatory means, it has attempted to create a protective wall to prevent or limit impairment, detect it early, and lessen its impacts. To effectively manage motor impairment illnesses in their early phases, this research largely focuses on creating sophisticated diagnostic techniques for early identification. As a result, early identification of motor health issues enables professionals to offer more potent treatments, enhancing the patient’s general health. To help specialists dealing with disabilities and motor disabilities, especially psychologists and psychiatrists, make judgments by examining patient behavior data and medical records, artificial intelligence (AI) and machine learning (ML) algorithms have been employed. To detect motor disability cases based on several accuracy criteria, this study identified and assessed the performance of six major ML algorithms: decision trees (DT), naïve Bayes (NB), k-nearest neighbors (K-NN), support vector machines (SVM), artificial neural networks (ANNs), and random forest (RF). The algorithms were evaluated using a standardized dataset obtained from the Kaggle website. The dataset was partitioned into a training set comprising 70 % of the data and a testing set containing 30 % of the data using the ML algorithms. The results of the implemented algorithms demonstrated that the RF technique attained an accuracy of 100 %, while the DT technique scored an accuracy of 63 %, the NB approach scored an accuracy of 69 %, the K-NN technique scored an accuracy of 76 %, and the SVM technique scored an accuracy of 90 %. The RF technique achieves the largest area under the curve, and the RF technique is the most effective of all ML algorithms, according to the results of the applied ML algorithms. This system aims to detect and forecast motor impairment disorders early on using AI model approaches. This system is an efficient tool that properly detects and diagnoses a variety of motor impairment problems using ML algorithms. Decisions are made easier and social health care is improved with the help of this system because timely interventions are implemented, patient outcomes are improved, and resource allocation is optimized. |
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| ISSN: | 1110-0168 |