scholarly journals Predicting 72-hour reattendance in emergency departments using discriminant analysis via mixed integer programming with electronic medical records

2019 ◽  
Vol 15 (2) ◽  
pp. 947-962 ◽  
Author(s):  
Fanwen Meng ◽  
◽  
Kiok Liang Teow ◽  
Kelvin Wee Sheng Teo ◽  
Chee Kheong Ooi ◽  
...  
2011 ◽  
Vol 10 (04) ◽  
pp. 589-612 ◽  
Author(s):  
MINGHE SUN

A mixed integer programming model is proposed for multiple-class discriminant and classification analysis. When multiple discriminant functions, one for each class, are constructed with the mixed integer programming model, the number of misclassified observations in the sample is minimized. This model is an extension of the linear programming models for multiple-class discriminant analysis but may be considered as a generalization of mixed integer programming formulations for two-class classification analysis. Properties of the model are studied. The model is immune from any difficulties of many mathematical programming formulations for two-class classification analysis, such as nonexistence of optimal solutions, improper solutions, and instability under linear data transformation. In addition, meaningful discriminant functions can be generated under conditions where other techniques fail. Examples are provided. Results on publically accessible datasets show that this model is very effective in generating powerful discriminant functions.


2009 ◽  
Vol 35 (2) ◽  
pp. 180-185
Author(s):  
Mi ZHAO ◽  
Zhi-Wu LI ◽  
Na WEI

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