{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T11:13:36Z","timestamp":1762341216427,"version":"build-2065373602"},"reference-count":37,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2019,1,15]],"date-time":"2019-01-15T00:00:00Z","timestamp":1547510400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Korean Ministry of Science and ICT","award":["2015-0-00938"],"award-info":[{"award-number":["2015-0-00938"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Recently, a standard dataset namely SCADI (Self-Care Activities Dataset) based on the International Classification of Functioning, Disability, and Health for Children and Youth framework for self-care problems identification of children with physical and motor disabilities was introduced. This is a very interesting, important and challenging topic due to its usefulness in medical diagnosis. This study proposes a robust framework using a sampling technique and extreme gradient boosting (FSX) to improve the prediction performance for the SCADI dataset. The proposed framework first converts the original dataset to a new dataset with a smaller number of dimensions. Then, our proposed framework balances the new dataset in the previous step using oversampling techniques with different ratios. Next, extreme gradient boosting was used to diagnose the problems. The experiments in terms of prediction performance and feature importance were conducted to show the effectiveness of FSX as well as to analyse the results. The experimental results show that FSX that uses the Synthetic Minority Over-sampling Technique (SMOTE) for the oversampling module outperforms the ANN (Artificial Neural Network) -based approach, Support vector machine (SVM) and Random Forest for the SCADI dataset. The overall accuracy of the proposed framework reaches 85.4%, a pretty high performance, which can be used for self-care problem classification in medical diagnosis.<\/jats:p>","DOI":"10.3390\/sym11010089","type":"journal-article","created":{"date-parts":[[2019,1,16]],"date-time":"2019-01-16T03:09:13Z","timestamp":1547608153000},"page":"89","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["A Robust Framework for Self-Care Problem Identification for Children with Disability"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0909-4974","authenticated-orcid":false,"given":"Tuong","family":"Le","sequence":"first","affiliation":[{"name":"Digital Contents Research Institute, Sejong University, Seoul 05006, Korea"}]},{"given":"Sung Wook","family":"Baik","sequence":"additional","affiliation":[{"name":"Digital Contents Research Institute, Sejong University, Seoul 05006, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2019,1,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Le, T., Le, H.S., Vo, M.T., Lee, M.Y., and Baik, S.W. (2018). A Cluster-Based Boosting Algorithm for Bankruptcy Prediction in a Highly Imbalanced Dataset. Symmetry, 10.","DOI":"10.3390\/sym10070250"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Le, T., Lee, M.Y., Park, J.R., and Baik, S.W. (2018). Oversampling techniques for bankruptcy prediction: Novel features from a transaction dataset. Symmetry, 10.","DOI":"10.3390\/sym10040079"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.engappai.2017.09.010","article-title":"Efficient algorithms for mining top-rank-k erasable patterns using pruning strategies and the subsume concept","volume":"68","author":"Le","year":"2018","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1007\/s10489-017-0986-0","article-title":"\u03b4-equality of intuitionistic fuzzy sets: A new proximity measure and applications in medical diagnosis","volume":"48","author":"Roan","year":"2018","journal-title":"Appl. Intell."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.bspc.2017.07.005","article-title":"Dental diagnosis from X-Ray images: An expert system based on fuzzy computing","volume":"39","author":"Le","year":"2018","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1016\/j.eswa.2017.09.027","article-title":"Segmentation of dental X-ray images in medical imaging using neutrosophic orthogonal matrices","volume":"91","author":"Ali","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1007\/s10916-018-0991-9","article-title":"Feature Selection for Automatic Tuberculosis Screening in Frontal Chest Radiographs","volume":"42","author":"Vajda","year":"2018","journal-title":"J. Med. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1007\/s10916-018-1003-9","article-title":"A Survey of Data Mining and Deep Learning in Bioinformatics","volume":"42","author":"Lan","year":"2018","journal-title":"J. Med. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1007\/s10916-018-0961-2","article-title":"A Novel Feature Level Fusion for Heart Rate Variability Classification Using Correntropy and Cauchy-Schwarz Divergence","volume":"42","author":"Goshvarpour","year":"2018","journal-title":"J. Med. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Pham, N.T., Lee, J.W., Kwon, G.R., and Park, C.S. (2018). Efficient image splicing detection algorithm based on markov features. Multimed. Tools Appl.","DOI":"10.1007\/s11042-018-6792-9"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Le, D.H., and Pham, V.H. (2017). HGPEC: A Cytoscape app for prediction of novel disease-gene and disease-disease associations and evidence collection based on a random walk on heterogeneous network. BMC Syst. Biol., 11.","DOI":"10.1186\/s12918-017-0437-x"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2219","DOI":"10.1016\/j.jmb.2018.05.006","article-title":"Annotating Diseases Using Human Phenotype Ontology Improves Prediction of Disease-Associated Long Non-coding RNAs","volume":"430","author":"Le","year":"2018","journal-title":"J. Mol. Biol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.eswa.2017.06.031","article-title":"A medical decision support system for disease diagnosis under uncertainty","volume":"88","author":"Malmir","year":"2017","journal-title":"Expert Syst. Appl."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.eswa.2018.03.024","article-title":"Improving Extreme Learning Machine by Competitive Swarm Optimization and its application for medical diagnosis problems","volume":"104","author":"Eshtay","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1016\/j.eswa.2017.02.023","article-title":"Insights from a machine learning model for predicting the hospital length of stay (los) at the time of admission","volume":"78","author":"Turgeman","year":"2017","journal-title":"Expert Syst. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.ijmedinf.2017.05.017","article-title":"Identification of key factors in consumers\u2019 adoption behavior of intelligent medical terminals based on a hybrid modified MADM model for product improvement","volume":"105","author":"Liu","year":"2017","journal-title":"Int. J. Med. Inform."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.ijmedinf.2017.10.008","article-title":"A statistical analysis-based recommender model for heart disease patients","volume":"108","author":"Mustaqeem","year":"2017","journal-title":"Int. J. Med. Inform."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ijmedinf.2017.01.001","article-title":"Text mining approach to predict hospital admissions using early medical records from the emergency department","volume":"100","author":"Lucini","year":"2017","journal-title":"Int. J. Med. Inform."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"977","DOI":"10.1177\/0898264309360573","article-title":"Physical disability and depression: Clarifying racial\/ ethnic contrasts","volume":"22","author":"Turner","year":"2010","journal-title":"J. Aging Health"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1097\/00004703-200508000-00012","article-title":"Diagnosis to function: Classification for children and youths","volume":"26","author":"Lollar","year":"2005","journal-title":"J. Dev. Behav. Pediatrics"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"605","DOI":"10.3109\/09638288.2010.505993","article-title":"Using the ICF-CY to organise characteristics of children\u2019s functioning","volume":"33","author":"Lee","year":"2011","journal-title":"Disabil. Rehabil."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1177\/1403494810378918","article-title":"Review article: Mapping of children\u2019s health and development data on population level using the classification system ICF-CY","volume":"39","author":"Granlund","year":"2011","journal-title":"Scand. J. Public Health"},{"key":"ref_23","unstructured":"Organization, W.H. (2007). International Classification of Functioning, Disability, and Health: Children & Youth Version: ICF-CY, World Health Organization."},{"key":"ref_24","unstructured":"Christiansen, C. (2000). Self-care Strategies for Children with Developmental Disabilities. Ways of Living: Self-Care Strategies for Special Needs, American Occupational Therapy Association. [2nd ed.]."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ijaz, M., Alfian, G., Syafrudin, M., and Rhee, J. (2018). Hybrid Prediction Model for Type 2 Diabetes and Hypertension Using DBSCAN-Based Outlier Detection, Synthetic Minority Over Sampling Technique (SMOTE), and Random Forest. Appl. Sci., 8.","DOI":"10.3390\/app8081325"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Bang, J., Hur, T., Kim, D., Lee, J., Han, Y., Banos, O., Kim, J.I., and Lee, S. (2018). Adaptive Data Boosting Technique for Robust Personalized Speech Emotion in Emotionally-Imbalanced Small-Sample Environments. Sensors, 18.","DOI":"10.3390\/s18113744"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.ijmedinf.2018.03.003","article-title":"SCADI: A standard dataset for self-care problems classification of children with physical and motor disability","volume":"114","author":"Zarchi","year":"2018","journal-title":"Int. J. Med. Inform."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Fern\u00e1ndez, A., Garc\u00eda, S., Galar, M., Prati, R.C., Krawczyk, B., and Herrera, F. (2018). Learning from Imbalanced Data Sets, Springer.","DOI":"10.1007\/978-3-319-98074-4"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1023\/A:1012406528296","article-title":"Support vector machines for classification in nonstandard situations","volume":"46","author":"Lin","year":"2002","journal-title":"Mach. Learn."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Liu, B., Ma, Y., and Wong, C. (2000, January 13\u201316). Improving an association rule-based classifier. Proceedings of the European Conference on Principles of Data Mining and Knowledge Discovery, PKDD, Lyon, France.","DOI":"10.1007\/3-540-45372-5_58"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic Minority Over-Sampling Technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"ref_32","first-page":"1","article-title":"Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning","volume":"18","author":"Lemaitre","year":"2017","journal-title":"J. Mach. Learn. Res."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1007\/s10618-008-0087-0","article-title":"Automatically countering imbalance and its empirical relationship to cost","volume":"17","author":"Chawla","year":"2008","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1055","DOI":"10.1109\/TKDE.2006.131","article-title":"Test strategies for cost-sensitive decision trees","volume":"18","author":"Ling","year":"2006","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1109\/TSMCC.2011.2161285","article-title":"A review on ensembles for class imbalance problem: Bagging, boosting and hybrid-based approaches","volume":"42","author":"Galar","year":"2012","journal-title":"IEEE Trans. Syst. Man Cybern. Part C"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1145\/1007730.1007735","article-title":"A Study of the Behavior of Several Methods for Balancing Machine Learning Training Data","volume":"6","author":"Batista","year":"2004","journal-title":"ACM SIGKDD Explor. Newsl."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, T. (2016, January 13\u201317). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/11\/1\/89\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:26:09Z","timestamp":1760185569000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/11\/1\/89"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,1,15]]},"references-count":37,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2019,1]]}},"alternative-id":["sym11010089"],"URL":"https:\/\/doi.org\/10.3390\/sym11010089","relation":{},"ISSN":["2073-8994"],"issn-type":[{"type":"electronic","value":"2073-8994"}],"subject":[],"published":{"date-parts":[[2019,1,15]]}}}