A Self-Care Prediction Model for Children with Disability Based on Genetic Algorithm and Extreme Gradient Boosting

Muhammad Syafrudin, Ganjar Alfian, Norma Latif Fitriyani, Muhammad Anshari, Tony Hadibarata, Agung Fatwanto and Jongtae Rhee
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Muhammad Syafrudin: Department of Industrial and Systems Engineering, Dongguk University, Seoul 04620, Korea
Ganjar Alfian: Industrial Artificial Intelligence (AI) Research Center, Nano Information Technology Academy, Dongguk University, Seoul 04626, Korea
Norma Latif Fitriyani: Department of Industrial and Systems Engineering, Dongguk University, Seoul 04620, Korea
Muhammad Anshari: School of Business & Economics, Universiti Brunei Darussalam, Gadong BE1410, Brunei
Tony Hadibarata: Department of Environmental Engineering, Faculty of Engineering and Science, Curtin University, Miri 98009, Malaysia
Agung Fatwanto: Informatika, Universitas Islam Negeri Sunan Kalijaga, Yogyakarta 55281, Indonesia
Jongtae Rhee: Department of Industrial and Systems Engineering, Dongguk University, Seoul 04620, Korea

Mathematics, 2020, vol. 8, issue 9, 1-21

Abstract: Detecting self-care problems is one of important and challenging issues for occupational therapists, since it requires a complex and time-consuming process. Machine learning algorithms have been recently applied to overcome this issue. In this study, we propose a self-care prediction model called GA-XGBoost, which combines genetic algorithms (GAs) with extreme gradient boosting (XGBoost) for predicting self-care problems of children with disability. Selecting the feature subset affects the model performance; thus, we utilize GA to optimize finding the optimum feature subsets toward improving the model’s performance. To validate the effectiveness of GA-XGBoost, we present six experiments: comparing GA-XGBoost with other machine learning models and previous study results, a statistical significant test, impact analysis of feature selection and comparison with other feature selection methods, and sensitivity analysis of GA parameters. During the experiments, we use accuracy, precision, recall, and f1-score to measure the performance of the prediction models. The results show that GA-XGBoost obtains better performance than other prediction models and the previous study results. In addition, we design and develop a web-based self-care prediction to help therapist diagnose the self-care problems of children with disabilities. Therefore, appropriate treatment/therapy could be performed for each child to improve their therapeutic outcome.

Keywords: ICF-CY; pattern classification; predictive models; algorithm design and analysis; feature selection; genetic algorithms; extreme gradient boosting (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
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