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    THE ASSESSMENT OF LH SURGE FOR PREDICTING OVULATION TIME USING CLINICAL, HORMONAL, AND ULTRASONIC INDEXES IN INFERTILE WOMEN WITH AN ENSEMBLE OF NEURAL NETWORKS
    (Pergamon-Elsevier Science Ltd, 1995) GURGEN, FS; SIHMANOGLU, M; VAROL, FG
    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 est radiol (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.