Data preparation for data mining
Data preparation for data mining
Medical Data Mining on the Internet: Research on a Cancer Information System
Artificial Intelligence Review - Special issue on data mining on the Internet
Machine Learning and Data Mining; Methods and Applications
Machine Learning and Data Mining; Methods and Applications
MPI collective algorithm selection and quadtree encoding
Parallel Computing
ICCIT '07 Proceedings of the 2007 International Conference on Convergence Information Technology
An Integrated Data Mining System for Patient Monitoring with Applications on Asthma Care
CBMS '08 Proceedings of the 2008 21st IEEE International Symposium on Computer-Based Medical Systems
Artificial intelligence techniques for monitoring dangerous infections
IEEE Transactions on Information Technology in Biomedicine
Proposing a Business Model in Healthcare Industry: E-Diagnosis
International Journal of Healthcare Information Systems and Informatics
Hi-index | 12.05 |
In this research we have used decision tree induction algorithm on Hospital Surveillance data to classify admitted patients according to their critical condition. Three class labels, low, medium and high, are used to distinguish the criticality of the admitted patients. Several decision tree models are developed, evaluated, and compared with different performance metrics. Finally an efficient classifier is developed to classify records and make decision/predictions on some input parameters. The models developed in this research could be helpful during epidemic when huge number of patients arrive daily. Due to rush of duty doctors and scarcity of required number of physicians, it is hard to diagnose every patient. Any computer application could be helpful to diagnose and measure the criticality of the newly arrived patient with the help of the historical data kept in the surveillance database. The application would ask few questions on physical condition and on history of disease of the patient and accordingly determines the critical condition of the patient as low, medium or high.