การลดจำนวนกลุ่มในการจำแนกแบบหลายกลุ่มเป็นสองกลุ่มสำหรับการจำแนกการกลับมารักษาซ้ำในโรงพยาบาลของผู้ป่วยโรคเบาหวาน
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Published:
Dec 29, 2019
Keywords:
logistic regressio decision tree multinomial classification multiclass classification binary classification diabetes patient
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Abstract
This research intends to compare the classification performance of re-hospitalization of diabetes patients using multiclass or multinomial classification and 2 cases of binary classification in logistic regression and decision tree techniques. The data used in the study are diabetes patients from Clinical Care at 130 US Hospitals and Integrated Delivery Networks. The patients were divided into 3 groups, i.e. not re-hospitalization, less than 30 days of re-hospitalization and more than 30 days of re-hospitalization. By comparing the classification techniques, it can be concluded that the classification by decision tree technique using 2 cases of binary classification yields the best result.
Article Details
Section
Physical Sciences
References
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[3] Bradley, E.H., Curry, L., Horwitz, L.I., Sipsma, H., Wang, Y., Walsh, M.N., Goldmann, D., White, N., Piña, I.L. and Krumholz, H.M., 2013, Hospital strategies associated with 30-day readmission rates for patients with heart failure, Circ. Cardiovasc. Qual. Outcomes 6: 444-450.
[4] Choowattanapakorn, T., Karuna, R., Konghan, S. and Tangmettajittakun, D., 2016, Factors predicting quality of life in older people with diabetes in Thailand, Songklanakarin J. Sci. Technol. 38: 707-714.
[5] Anitha, J. and Pethalakshmi, A., 2017, Comparison of classification algorithms in diabetic dataset, The 5th National Conference on Computational Methods, Communication Techniques and Informatics Department of Computer Science & Applications, The Gandhigram Rural Institute-Deemed University, Tamil Nadu.
[6] Chen, H., Zhang, J., Xu, Y., Chen, B. and Zhang, K., 2012, Performance comparison of artificial neural network and logistic regression model for differentiating lung nodules on CT scans, J. Med. Eng. Technol. 39: 11503-11509.
[7] Dogan, N. and Tanrikulu, Z., 2013, A comparative analysis of classification algorithms in data mining for accuracy, speed and robustness, Inf. Technol. Manage. 14: 105-124.
[8] Eftekhar, B., Mohammad, K., Ardebili, H. E., Ghodsi, M. and Ketabchi, E., 2005, Comparison of artificial neural network and logistic regression models for prediction of mortality in head trauma based on initial clinical data, BMC Med. Inform. Decis. Mak. 5: 1-8.
[9] Thangaraju, P., Deepa, B. and Karthikeyan, T., 2014, Comparison of data mining techniques for forecasting diabetes mellitus, IJARCSEE 3: 7674-7677.
[10] Srinivas, M., Bharath, R., Rajalakshmi, P. and Mohan, C.K., 2015, Multi-level classification: A generic classification method for medical datasets, The 17th International Conference on E-health Networking, Application & Services (HealthCom).
[11] Hosmer, Jr.D.W., Lemeshow, S. and Sturdivant, R.X., 2013, Applied Logistic Regression, John Wiley & Sons, New York, 398 p.
[12] James, G., Witten, D., Hastie, T. and Tibshirani, R., 2013, An Introduction to Statistical Learning with Applications in R, Springer, 426 p.
[13] Cichosz, P., 2015, Data mining algorithms: Explained using R, Chichester, West Sussex, Wiley, Chichester, West Sussex, Wiley, 683 p.
[14] Jitthavech, J., 2015, Regression Analysis, Bangkok, National Institute of Development Administration, 433 p. (in Thai)
[15] Maiprasert, D., 2013, Prediction of breast cancer stage using multinomial logistic regression and artificial neural network, Ph.D. Thesis, Rangsit University, Bangkok, 215 p. (in Thai)
[16] Pacharawongsakda, E., 2014, An Introduction to Data Mining Techniques. Bangkok, Asia Digital Press Co., Ltd., 124 p. (in Thai)
[17] Grisanti, J., Decision Trees: An Overview. Available Source: https://www.aunalytics.com/2015/01/30/decision-trees-an-overvi ew, June 9, 2015.
[18] Strack, B., DeShazo, J.P., Gennings, C., Olmo, J.L., Ventura, S., Cios, K.J. and Clore, J.N., 2014, Impact of HbA1c measurement on hospital readmission rates: Analysis of 70,000 clinical database patient records, Biomed. Res. Int. 2014: 1-11.
[19] Cotha, N.K.P. and Sokolova, M., 2015, Multi-Labeled Classification of Demographic Attributes of Patients: A Case Study of Diabetics Patients, pp. 1-16, Cornall University Library, New York.
[20] Jelinek, H., Abawajy, J., Kelarev, A., Chowdhury, M. and Stranieri, A., 2013, Decision trees and multi-level ensemble classifiers for neurological diagnostics, AIMS Med. Sc. 1: 1-12.
[21] Bihis, M. and Roychowdhury, S., 2015, A generalized flow for multi-class and binary classification tasks: An Azure ML approach, pp. 1728-1737, The 2015 IEEE International Conference on Big Data.
[22] Beleites, C., Salzer, R. and Sergo, V., 2013, Validation of soft classification models using partial class memberships: An extended concept of sensitivity & co. applied to grading of astrocytoma tissues, Chemometr. Intell. Lab. Syst. 122: 12-22.
[23] Ebrahimi, N., 2008, Simultaneous control of false positives and false negatives in multiple hypotheses testing, J. Multivar. Anal. 99: 437-450.
[24] Zhao, C., Crothers, B.A., Tabatabai, Z.L., Li, Z., Ghofrani, M., Souers, R.J., Husain, M., Fan, F., Shen, R. and Ocal, I.T., 2017, False-negative interpretation of adenocarcinoma in situ in the college of American Pathologists Gynecologic PAP Education Program, Arch. Pathol. Lab. Med. 141: 666-670.