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Artificial neural networks are computer software systems that recognize patterns in complex data sets. A recent development in neural computing, multiversion systems (MVS), has led to enhanced analytical power, and this was harnessed to demonstrate the value of risk factors in predicting the result of osteoporosis investigations by quantitative ultrasound. 274 women were screened in an open-access osteoporosis service. A conventional risk factor questionnaire was completed for each patient by the osteoporosis specialist nurse. An MVS was trained on 180 randomly selected data sets and tested on the remaining 94. The results were compared with those from logistic regression analysis in predictive power, both from the selected 20-item questionnaire and for a limited 5-item questionnaire comprising age, height, height loss, weight and years since the menopause. The MVS approach predicted the T-score categorization of the patients from the 20-item questionnaire with 83.0% accuracy, whereas logistic regression yielded an accuracy of only 72.8% (P = 0.04). From the 5-item database the MVS yielded a best prediction accuracy of 73.1%, whereas the logistic regression prediction accuracy was 60% (P = 0.04). These results suggest that 20 risk factors can be used by an MVS to predict the outcome of osteoporosis investigations with a power that outperforms conventional statistical methods. Use of this system may improve the selection of patients for osteoporosis investigations, since even with only 5 risk factors the system performs nearly as well as that based on the full 20 factors.