Critical condition classification of patients from ICCDR,B hospital surveillance data Online publication date: Mon, 10-Jul-2017
by Adnan Firoze; Rashedur M. Rahman
International Journal of Advanced Intelligence Paradigms (IJAIP), Vol. 9, No. 4, 2017
Abstract: During epidemic, when large number of patients appears in short interval, computer models could help in predicting the critical condition of newly admitted patients based on historical information of similar type of patients. In this research, we have developed two classification models by neural network and logistic regression to predict the critical condition of newly admitted patients. Three class labels, i.e., low, medium and high are used in this research to represent the critical condition of patients. However, due to class imbalance problem, the classifier performance was not good for high and mid classes. Therefore, a balancing technique is adopted by using synthetic minority over-sampling technique (SMOTE) algorithm coupled with locally linear embedding (LLE). Experimental results demonstrate that our balanced model outperforms other models by taking care the unbalance nature of ICDDR,B hospital surveillance data.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Advanced Intelligence Paradigms (IJAIP):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook