Neural-network-based decision making in diagnostic applications
IEEE Engineering in Medicine and Biology Magazine, cilt.18, sa.4, ss.89-93, 1999 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 18 Sayı: 4
- Basım Tarihi: 1999
- Doi Numarası: 10.1109/51.775493
- Dergi Adı: IEEE Engineering in Medicine and Biology Magazine
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
- Sayfa Sayıları: ss.89-93
- Boğaziçi Üniversitesi Adresli: Evet
Özet
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. The neural network (NN) approach for medical decision making was applied to three examples. The first example was decision making with single-valued data for intrauterine growth retardation (IUGR) detection. The second example was decision making with double-valued data in prediction of ovulation. The third involved the use of independent NN modules and consensus theory for prediction of ovulation time. In all cases, the NN approach had superiority over classical statistical approaches for decision making with medical data.