title: Employing Data from Diagnosed Patients for Undiagnosed Type 2 Diabetes Detection.
abstract: In this study, it is aimed to generate improved models for detecting undiagnosed type II diabetes mellitus patients by employing data from both undiagnosed and diagnosed patients. The main motivation is that, with the widespread use of electronic health records, increasing amount of data is accumulating for diagnosed patients. In our simulations, the training sets are allowed to include data from both undiagnosed and diagnosed patients. The models generated are then evaluated using the undiagnosed patients. Experimental results have shown that the data from diagnosed patients do not help to improve the detection performance of undiagnosed patients.
authors.affiliation: Mobile Health Research and Application Center, Department of Computer Engineering, Eastern Mediterranean University, Famagusta, Cyprus.
authors.forename: Noushin
authors.initials: N
authors.lastname: Hajarolasvadi
authors.affiliation: Mobile Health Research and Application Center, Department of Computer Engineering, Eastern Mediterranean University, Famagusta, Cyprus.
authors.forename: Ghazaal
authors.initials: G
authors.lastname: Sheikhi
authors.affiliation: Mobile Health Research and Application Center, Department of Computer Engineering, Eastern Mediterranean University, Famagusta, Cyprus.
authors.forename: Hakan
authors.initials: H
authors.lastname: Altinçay
mesh_terms: D001786:Blood Glucose; D003198:Computer Simulation; D003924:Diabetes Mellitus, Type 2 / Q000175:diagnosis*; D057286:Electronic Health Records / Q000706:statistics & numerical data*; D006442:Glycated Hemoglobin; D016015:Logistic Models; D011236:Prediabetic State / Q000175:diagnosis*; D012307:Risk Factors; D012680:Sensitivity and Specificity
chemical_list: D001786:Blood Glucose; D006442:Glycated Hemoglobin