Impact of missing data in evaluating artificial neural networks trained on complete data.
This study investigated the impact of missing data in the evaluation of artificial neural network (ANN) models trained on complete data for the task of predicting whether breast lesions are benign or malignant from their mammographic Breast Imaging and Reporting Data System (BI-RADS) descriptors. A feed-forward, back-propagation ANN was tested with three methods for estimating the missing values. Similar results were achieved with a constraint satisfaction ANN, which can accommodate missing values without a separate estimation step. This empirical study highlights the need for additional research on developing robust clinical decision support systems for realistic environments in which key information may be unknown or inaccessible.
Markey
Mia K
MK
Biomedical Engineering Department, The University of Texas at Austin, 1 University Station, C0800, ENS617B, Austin, TX 78712, USA. mia.markey@mail.utexas.edu
Tourassi
Georgia D
GD
Margolis
Michael
M
DeLong
David M
DM
D019540:Area Under Curve; D001185:Artificial Intelligence; D001943:Breast Neoplasms / Q000175:diagnosis*; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted; D005260:Female; D006801:Humans; D008327:Mammography / Q000379:methods*; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D012372:ROC Curve; D011857:Radiographic Image Interpretation, Computer-Assisted; D012680:Sensitivity and Specificity; D012984:Software