Informatics in radiology: comparison of logistic regression and artificial neural network models in breast cancer risk estimation.
Computer models in medical diagnosis are being developed to help physicians differentiate between healthy patients and patients with disease. These models can aid in successful decision making by allowing calculation of disease likelihood on the basis of known patient characteristics and clinical test results. Two of the most frequently used computer models in clinical risk estimation are logistic regression and an artificial neural network. A study was conducted to review and compare these two models, elucidate the advantages and disadvantages of each, and provide criteria for model selection. The two models were used for estimation of breast cancer risk on the basis of mammographic descriptors and demographic risk factors. Although they demonstrated similar performance, the two models have unique characteristics-strengths as well as limitations-that must be considered and may prove complementary in contributing to improved clinical decision making.
Ayer
Turgay
T
Departments of Industrial and Systems Engineering, Radiology, and Biostatistics and Medical Informatics, University of Wisconsin, 1513 University Ave., Madison, WI 53706-1572, USA.
Chhatwal
Jagpreet
J
Alagoz
Oguzhan
O
Kahn
Charles E
CE
Woods
Ryan W
RW
Burnside
Elizabeth S
ES
D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D016015:Logistic Models; D008327:Mammography / Q000379:methods; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D012044:Regression Analysis; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000379:methods; D012307:Risk Factors; D012680:Sensitivity and Specificity