Implementation of various data processing and evaluation techniques on ICDDR, B surveillance data to generate optimal decision tree for patients classification. (2nd March 2012)

• Record Type: Journal Article
• Authors: Rahman, Rashedur M.; Hasan, Fazle Rabbi Md.
• Is Part Of: International journal of knowledge engineering and soft data paradigms. Volume 3:Number 2(2011)
• Page Start: 152
• Page End: 187
• Publication Date: 2012-03-02
• Subjects: data mining -- classification -- decision tree -- performance analysis -- accuracy -- surveillance system -- medical diagnostic -- patients classification -- precision -- decision making; Soft computing -- Periodicals; Statistics -- Periodicals; Information science -- Periodicals; 003.05
• Languages: English
• ISSNs: 1755-3210

Abstract

The surveillance system established in ICDDR, B collect information of diarrhoeal disease. Our research focuses on generating decision tree models to categorise diarrhoeal patients according to the severance of disease. From the decision tree generated based on earlier cases stored in the surveillance data, decision rules are generated. These rules are used to classify patients into three classes according to their criticality: high, mid, low. This would help the hospital authority to take prudent actions on critical patients. Different techniques are used to build an optimal decision tree by considering various set of data, and generated trees are compared with various performance metrics, e.g., accuracy, precision, recall, area under ROC curve, etc.