Neural-network-based decision making in diagnostic applications.
In this article the NN approach for medical decision making was applied for three specific examples. The first example was decision making with single-valued data for IUGR detection. The second example was decision making with double-valued data in prediction of ovulation. The third example was the use of independent NN modules and consensus theory for prediction of ovulation time. The NN approach has superiority over classical statistical approaches for decision making with medical data for the following reasons: 1. It is distribution-free. 2. It captures correlative features and does not need any specific consideration for mutual test dependence. 3. It provides weighted reliability of various tests. 4. It produces fast, accurate results. The statistical decision approach will probably outperform the NN approach in making decisions when an accurate distribution model is provided. However, the NN is proposed as a useful tool to help physicians in decision making and diagnosis of certain symptoms. The capability and performance of this tool has generally been proven in combining mutually dependent medical tests.
Gürgen
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Computer Engineering Dept., Bogazici University. gurgen@boun.edu.tr
D000465:Algorithms; D003198:Computer Simulation; D003657:Decision Making; D003936:Diagnosis, Computer-Assisted; D004958:Estradiol / Q000097:blood; D005260:Female; D005317:Fetal Growth Retardation / Q000175:diagnosis; D006801:Humans; D007986:Luteinizing Hormone / Q000097:blood; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D006080:Ovarian Follicle / Q000033:anatomy & histology; D010060:Ovulation / Q000502:physiology; D010061:Ovulation Detection; D011336:Probability; D011374:Progesterone / Q000097:blood; D015203:Reproducibility of Results; D013223:Statistics as Topic; D016216:Ultrasonography, Prenatal
D011374:Progesterone; D004958:Estradiol; D007986:Luteinizing Hormone