The assessment of LH surge for predicting ovulation time using clinical, hormonal, and ultrasonic indices in infertile women with an ensemble of neural networks
Computers in Biology and Medicine, cilt.25, sa.4, ss.405-413, 1995 (Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 25 Sayı: 4
- Basım Tarihi: 1995
- Doi Numarası: 10.1016/0010-4825(95)00022-v
- Dergi Adı: Computers in Biology and Medicine
- Derginin Tarandığı İndeksler: Scopus
- Sayfa Sayıları: ss.405-413
- Anahtar Kelimeler: Assessment of LH surge, Clinical, Consensus of multisource data, Ensemble of neural networks, hormonal and ultrasonic indices, Prediction of ovulation time
- Boğaziçi Üniversitesi Adresli: Evet
Özet
An ensemble of independently trained neural networks (NN) is proposed for the assessment of luteinizing hormone (LH) surge for predicting ovulation time in infertile but ovulating women. The proposed ensemble involves a number of parallel NN modules. Each pair of the NNs learn specific data that are previously collected for monitoring timing function of LH levels. Training data which correspond to values of serum progesterone (ng ml-1), serum estradiol (pg ml-1), and follicle diameter (mm) are used to train NN pairs to approximate the function of the LH values. A reasonable and accurate estimation places ovulation approximately 10-12 h after the LH peak. The double-valued (bi-phasic) regions of training data are separated into two single-valued (bi-phasic) regions of training data are separated into two single-valued parts (not exactly preovulatory, postovulatory division) that can be learned by each module of the NN pair. During testing, after the initial decision to have single-valued sides, the assessment is obtained by a linear opinion pool (consensus rule) using the decisions of NNs on the corresponding side without waiting. The network ensemble has various desirable properties: high assessment accuracy of a double-valued multisource data, minimized learning and recall times, and a parallel structure. The ovulation time can be predicted through the assessment of LH peak with a better precision and fewer number of tests. © 1995.