Missing data imputation for individualised CVD diagnostic and treatment

10Citations
Citations of this article
10Readers
Mendeley users who have this article in their library.

Abstract

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.

Cite

CITATION STYLE

APA

Venkatraman, S., Yatsko, A., Stranieri, A., & Jelinek, H. F. (2016). Missing data imputation for individualised CVD diagnostic and treatment. In Computing in Cardiology (Vol. 43, pp. 349–352). IEEE Computer Society. https://doi.org/10.22489/cinc.2016.100-179

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free