Enhanced laboratory diagnosis of human chlamydia pneumoniae infection through pattern recognition derived from pathology database analysis
datasetposted on 21.11.2017 by Richardson, Alice, Hawkins, Simon, Shadabi, Fariba, Sharma, Dharmendra, Fulcher, John, Lidbury, Brett A.
Datasets usually provide raw data for analysis. This raw data often comes in spreadsheet form, but can be any collection of data, on which analysis can be performed.
This study focuses on pattern recognition in pathology data collected from patients tested for Chlamydia pneumoniae (Cp) infection, with co-infection by Mycoplasma pneumoniae (Myco) also considered. Both Cp and Myco are microbes that cause respiratory disease in some infected people. As well as the immunoassay results revealing whether the patient had been infected, or not, an extensive range of other routine pathology data was also available for each patient, allowing the analysis of associations between a positive immunoassay laboratory result for Cp or Myco, and a range of tests for biochemical and cellular markers (e.g. liver enzymes, electrolyte balance, haematological indices such as red/white cell counts). Decision trees and logistic regression were used to enhance laboratory diagnosis of these respiratory infections via the formulation of association rules derived from immunoassay results and associated pathology data. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1 Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; Chetty, Madhu ; Ahmad, Shandar ; Ngom, Alioune ; Teng, Shyh Wei ; Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; Coverage: Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.