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

Muhammad Syafrudin,Tony Hadibarata,Muhammad Anshari,Jongtae Rhee,Ganjar Alfian,Norma Latif Fitriyani,Agung Fatwanto

doi:10.3390/math8091590

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.

Highlights

Children with motor and physical disabilities have issues with daily living, and self-care as the main daily need more consideration for these children
The contributions of our study can be summarized as follows: (i) we propose a combined method of genetic algorithms (GAs) and XGBoost for self-care prediction, which has never been done before; (ii) we improve the performance of the proposed model by adjusting the population size of GA; (iii) we conduct extensive comparative experiments on the proposed model with other prediction models and previous study results; (iv) we perform a two-step statistical significance test to verify the performance of proposed model; (v) we provide the impact analysis of feature selection method with or without GA as well as other feature selection methods toward model’s accuracy performance; and (vi) we show the practicability of our proposed model by designing and developing the web-based self-care prediction model
This study presents a self-care prediction model known as GA-XGBoost, which combined algorithms (GA) with extreme gradient boosting (XGBoost) for predicting self-care problems of children genetic algorithms (GA) with extreme gradient boosting (XGBoost) for predicting self-care problems with disabilities

Summary

Introduction

Children with motor and physical disabilities have issues with daily living, and self-care as the main daily need more consideration for these children. Since self-care classification is a complex and time-consuming process, it has become a major and challenging issue, especially when there is a shortage of expert occupational therapists [1]. Decision tools could be used to support therapist in diagnosing and classifying the self-care problems so that the appropriate treatment could be performed for each child [2]. Mathematics 2020, 8, 1590 framework called ICF-CY, which stands for international classification of functioning, disability, and health for children and youth (ICF-CY). This framework has been used as a standardized guideline to classify self-care problems [3]. Several previous studies have utilized machine learning algorithms, such as the artificial neural network (ANN) [4], k-nearest neighbor (KNN) [5], naïve Bayes (NB) [6], extreme gradient boosting (XGBoost) [7], fuzzy neural networks (FNN) [8], deep neural networks (DNN) [9], and hybrid autoencoder [10] to help occupational therapists improve the classification accuracy and reducing the time as well as the cost of self-care classification [11]

Objectives

Results

Conclusion