TY - GEN
T1 - Logistic regression analysis for predicting methicillin-resistant staphylococcus aureus (MRSA) in-hospital mortality
AU - Hai, Yizhen
AU - Cheng, Vincent Cc
AU - Wong, Shui Yee
AU - Tsui, Kwok Leung
AU - Yuen, Kwok Yung
PY - 2011
Y1 - 2011
N2 - Statistical models have been widely used in public health and made a difference in a wide range of applications. For example, they provide new ideas for efficient feature selection. This paper attempts to demonstrate how to apply regression-based methods to accurately predict in-hospital mortality of Methicillin-resistant Staphylococcus Aureus (MRSA) patients. Logistic regression is used to predict the in-hospital death. It is found that admission age, residency, solid tumor, hemic malignancy, COAD, Dementia, PLT, Lymphocyte, Urea, and ALP are the significant prognostic factors (P<0.1) for in-hospital survival. Using cross validation and random splitting and the prediction accuracy is around 85%. The future research direction is to strengthen the robustness of the predictive model. Possible direction is to make use of other data mining "blackbox" methods, such as k-NN and SVM. These models also need further validation on their performance and feature selection.
AB - Statistical models have been widely used in public health and made a difference in a wide range of applications. For example, they provide new ideas for efficient feature selection. This paper attempts to demonstrate how to apply regression-based methods to accurately predict in-hospital mortality of Methicillin-resistant Staphylococcus Aureus (MRSA) patients. Logistic regression is used to predict the in-hospital death. It is found that admission age, residency, solid tumor, hemic malignancy, COAD, Dementia, PLT, Lymphocyte, Urea, and ALP are the significant prognostic factors (P<0.1) for in-hospital survival. Using cross validation and random splitting and the prediction accuracy is around 85%. The future research direction is to strengthen the robustness of the predictive model. Possible direction is to make use of other data mining "blackbox" methods, such as k-NN and SVM. These models also need further validation on their performance and feature selection.
KW - K-nearest Neighbour Algorithm
KW - Logistic Regression
KW - Methicillin-resistant Staphylococcus aureus (MRSA)
KW - Prognostication
UR - http://www.scopus.com/inward/record.url?scp=80052883922&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80052883922&partnerID=8YFLogxK
U2 - 10.1109/ISI.2011.5984112
DO - 10.1109/ISI.2011.5984112
M3 - Conference contribution
AN - SCOPUS:80052883922
SN - 9781457700828
T3 - Proceedings of 2011 IEEE International Conference on Intelligence and Security Informatics, ISI 2011
SP - 349
EP - 353
BT - Proceedings of 2011 IEEE International Conference on Intelligence and Security Informatics, ISI 2011
T2 - 2011 IEEE International Conference on Intelligence and Security Informatics, ISI 2011
Y2 - 10 July 2011 through 12 July 2011
ER -