A new method for modeling preoperative diagnosis of ovarian tumors.
In this paper, we present a sequential nonuniform procedure, an inference method which combines feature selection based on the Kullback information gain and a step-wise classification procedure to produce a reliable, interpretable, and robust model. We applied the model to an ovarian tumor data set to distinguish between malignant and benign tumors. The performance of the model was assessed using receiver operating characteristic (ROC) analysis and gave an overall accuracy over 85%, and area under the curve (AUC) of 0.887 which compares well with existing methods. The method presented here is significant because of its ability to handle missing values, and it only uses a small number of variables which are graded according to their discriminative relevance. This, together with the fact that the resulting model is interpretable and has good performance, is likely to lead to widespread clinical acceptance of the method. The method is also generic and can be readily adapted for other classifications problems in biomedicine.
Stalbovskaya
Viktoriya
V
Signal Processing and Multimedia Communications Group, School of Computing, Communications and Electronics, University of Plymouth, Plymouth PL4 8AA, UK. vstalbovskaya@plymouth.ac.uk
Ifeachor
Emmanuel C
EC
Van Huffel
Sabine
S
Timmerman
Dirk
D
D001185:Artificial Intelligence; D003627:Data Interpretation, Statistical; D020000:Decision Support Systems, Clinical; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D006801:Humans; D008954:Models, Biological; D015233:Models, Statistical; D010051:Ovarian Neoplasms / Q000175:diagnosis* / Q000503:physiopathology*; D011300:Preoperative Care / Q000379:methods*; D011379:Prognosis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity