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A Neural Net/Genetic Algorithm Model to Predict Caesarean Section in a Busy Labor Ward

Dr. Ken Giuffre, Director of Anesthesia Research, and his research team at Hackensack University Medical Center of UMDNJ, have constructed a model that predicts caesarean sections on a busy labor ward. Their paper was presented at the 1998 meeting of the American Society of Anesthesiologists, and full publication is pending their use addition of statistics (ANOVA) and a training set of 1000 patients. Following is the summary of their paper:

A Neural Net/Genetic Algorithm Model to Predict Caesarean Section in a Busy Labor Ward.
Giuffre KA, et.al.
Department of Anesthesiology
Hackensack University Medical Center,
30 Prospect Ave.
Hackensack NJ 07601

Using computer-based artificial neural networks (ANN), we sought to determine if epidural analgesia influences caesarean section (c/s) rates while creating a general predictive model of c/s in a diverse obstetric population.

We used 46 variables taken from 162 charts of consecutive parturients admitted to HUMC for labor and delivery in March 1997 for model training. Two additional and separate chart groups of 20 and 38 patients were used to test the model's predictive value respectively before and after removing patients who had undergone prior c/s. Backpropagation neural networks (BNN, NeuroShell 2, Ward Systems Group, Frederick MD) alone had no predictive value. A separate classification model (Kohonen net, NeuroShell 2, Ward Systems Group) revealed patients with a prior c/s as comprising a different group with a c/s rate of 62% (p<0.001). After removing patients who had undergone prior c/s from the teaching and test sets, and constructing a classification neural net with genetic-based alterations (NeuroShell Classifier, Ward Systems Group). We created a highly predictive model that utilized 9 inputs and correctly classified 32 of 38 (84%) of patients in the test group. Of the 6 missed classifications, 1 was a false negative (specificity 97%) and 5 were false positives (sensitivity 87%). No relationship between epidural analgesia and c/s was seen the final training group from which patients experiencing prior c/s were removed.

We conclude that: 1) ANN modeling of patient data is more effective when utilizing a system that employs a genetic algorithm to fine-tune net architecture. 2) It is possible to train an ANN data-model that generalizes well enough to a test group for predicting c/s. 3) Epidural analgesia in patients with no prior c/s history has no effect on c/s rates.

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