A comparison of supervised machine learning techniques for predicting short-term in-hospital length of stay among diabetic patients

April Morton; Eman Marzban; Georgios Giannoulis; Ayush Patel; Rajender Aparasu; Ioannis A. Kakadiaris

doi:10.1109/ICMLA.2014.76

A comparison of supervised machine learning techniques for predicting short-term in-hospital length of stay among diabetic patients

April Morton, Eman Marzban, Georgios Giannoulis, Ayush Patel, Rajender Aparasu, Ioannis A. Kakadiaris

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

52 Scopus citations

Abstract

Diabetes is a life-altering medical condition that affects millions of people and results in many hospitalizations per year. Consequently, predicting the length of stay of in-hospital diabetic patients has become increasingly important for staffing and resource planning. Although statistical methods have been used to predict length of stay in hospitalized patients, many powerful machine learning techniques have not yet been explored. In this paper, we compare and discuss the performance of various supervised machine learning algorithms (i.e., Multiple linear regression, support vector machines, multi-task learning, and random forests) for predicting long versus short-term length of stay of hospitalized diabetic patients.

Original language	English (US)
Title of host publication	Proceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014
Editors	Cesar Ferri, Guangzhi Qu, Xue-wen Chen, M. Arif Wani, Plamen Angelov, Jian-Huang Lai
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	428-431
Number of pages	4
ISBN (Electronic)	9781479974153
DOIs	https://doi.org/10.1109/ICMLA.2014.76
State	Published - Feb 5 2014
Event	2014 13th International Conference on Machine Learning and Applications, ICMLA 2014 - Detroit, United States Duration: Dec 3 2014 → Dec 6 2014

Publication series

Name	Proceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014

Conference

Conference	2014 13th International Conference on Machine Learning and Applications, ICMLA 2014
Country/Territory	United States
City	Detroit
Period	12/3/14 → 12/6/14

Keywords

Diabetes
In-Hospital Length of Stay Prediction
Multi-Task Learning
Random Forests
Supervised Machine Learning
Support Vector Machines
Support Vector Machines Plus

ASJC Scopus subject areas

Computer Science Applications
Human-Computer Interaction

Access to Document

10.1109/ICMLA.2014.76

Cite this

Morton, A., Marzban, E., Giannoulis, G., Patel, A., Aparasu, R., & Kakadiaris, I. A. (2014). A comparison of supervised machine learning techniques for predicting short-term in-hospital length of stay among diabetic patients. In C. Ferri, G. Qu, X. Chen, M. A. Wani, P. Angelov, & J.-H. Lai (Eds.), Proceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014 (pp. 428-431). Article 7033154 (Proceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICMLA.2014.76

A comparison of supervised machine learning techniques for predicting short-term in-hospital length of stay among diabetic patients. / Morton, April; Marzban, Eman; Giannoulis, Georgios et al.
Proceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014. ed. / Cesar Ferri; Guangzhi Qu; Xue-wen Chen; M. Arif Wani; Plamen Angelov; Jian-Huang Lai. Institute of Electrical and Electronics Engineers Inc., 2014. p. 428-431 7033154 (Proceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Morton, A, Marzban, E, Giannoulis, G, Patel, A, Aparasu, R & Kakadiaris, IA 2014, A comparison of supervised machine learning techniques for predicting short-term in-hospital length of stay among diabetic patients. in C Ferri, G Qu, X Chen, MA Wani, P Angelov & J-H Lai (eds), Proceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014., 7033154, Proceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014, Institute of Electrical and Electronics Engineers Inc., pp. 428-431, 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014, Detroit, United States, 12/3/14. https://doi.org/10.1109/ICMLA.2014.76

Morton A, Marzban E, Giannoulis G, Patel A, Aparasu R, Kakadiaris IA. A comparison of supervised machine learning techniques for predicting short-term in-hospital length of stay among diabetic patients. In Ferri C, Qu G, Chen X, Wani MA, Angelov P, Lai JH, editors, Proceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014. Institute of Electrical and Electronics Engineers Inc. 2014. p. 428-431. 7033154. (Proceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014). doi: 10.1109/ICMLA.2014.76

Morton, April ; Marzban, Eman ; Giannoulis, Georgios et al. / A comparison of supervised machine learning techniques for predicting short-term in-hospital length of stay among diabetic patients. Proceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014. editor / Cesar Ferri ; Guangzhi Qu ; Xue-wen Chen ; M. Arif Wani ; Plamen Angelov ; Jian-Huang Lai. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 428-431 (Proceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014).

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A comparison of supervised machine learning techniques for predicting short-term in-hospital length of stay among diabetic patients

Abstract

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Keywords

ASJC Scopus subject areas

Access to Document

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