Missing data imputation for individualised CVD diagnostic and treatment

Sitalakshmi Venkatraman, Andrew Yatsko, Andrew Stranieri, Herbert F. Jelinek

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

9 Scopus citations


Cardiac health screening standards require increasingly more clinical tests consisting of blood, urine and anthropometric measures as well as an extensive clinical and medication history. To ensure optimal screening referrals, diagnostic determinants need to be highly accurate to reduce false positives and ensuing stress to individual patients. However, the data from individual patients partaking in population screening is often incomplete. The current study provides an imputation algorithm that has been applied to patient-centered cardiac health screening. Missing values are iteratively imputed in conjunction with combinations of values on subsets of selected features. The approach was evaluated on the DiabHealth dataset containing 2800 records with over 180 attributes. The results for predicting CVD after data completion showed sensitivity and specificity of 94% and 99% respectively. Removing variables that define cardiac events and associated conditions directly, left 'age' followed by 'use' of anti-hypertensive and anti-cholesterol medication, especially statins among the best predictors.

Original languageBritish English
Title of host publicationComputing in Cardiology Conference, CinC 2016
EditorsAlan Murray
PublisherIEEE Computer Society
Number of pages4
ISBN (Electronic)9781509008964
StatePublished - 1 Mar 2016
Event43rd Computing in Cardiology Conference, CinC 2016 - Vancouver, Canada
Duration: 11 Sep 201614 Sep 2016

Publication series

NameComputing in Cardiology
ISSN (Print)2325-8861
ISSN (Electronic)2325-887X


Conference43rd Computing in Cardiology Conference, CinC 2016


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