Abstract
This research effort deals with the application of Artificial Neural Networks (ANNs) in order to help the diagnosis of cases with an orthopaedic disease, namely osteoporosis. Probabilistic Neural Networks (PNNs) and Learning Vector Quantization (LVQ) ANNs, were developed for the estimation of osteoporosis risk. PNNs and LVQ ANNs are both feed-forward networks; however they are diversified in terms of their architecture, structure and optimization approach. The obtained results of successful prognosis over pathological cases lead to the conclusion that in this case the PNNs (96.58%) outperform LVQ (96.03%) networks, thus they provide an effective potential soft computing technique for the evaluation of osteoporosis risk. The ANN with the best performance was used for the contribution assessment of each risk feature towards the prediction of this medical disease. Moreover, the available data underwent statistical processing using the Receiver Operating Characteristic (ROC) analysis in order to determine the most significant factors for the estimation of osteoporosis risk. The results of the PNN model are in accordance with the ROC analysis and identify age as the most significant factor.
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Dayhoff, J., DeLeo, J.: Artificial Neural Networks Opening the Black Box. Cancer Supplement 91, 1615–1635 (2001)
Iliadis, L.: An Intelligent Artificial Neural Network Evaluation System Using Fuzzy Set Hedges: Application in Wood Industry. In: The Annual IEEE International Conference on Tools with Artificial Intelligence (2007)
Hisashi, A., Tsuyoshi, O., Takahashi, N., Tanaka, M.: Sigma-Delta Cellular Neural Network for 2D Modulation. Neural Networks 21, 349–357 (2008)
Haralambous, H., Papadopoulos, H.: 24-hour Neural Network Congestion Models for Highfrequency Broadcast Users. IEEE Transactions on Broadcasting 55, 145–154 (2009)
Mantzaris, D., Anastassopoulos, G., Lymperopoulos, K.: Medical Disease Prediction Using Artificial Neural Networks. In: 8th IEEE International Conference on BioInformatics and BioEngineering (2008)
Papadopoulos, H., Gammerman, A., Vovk, V.: Confidence Predictions for the Diagnosis of Acute Abdominal Pain. In: AIAI 2009, pp. 175–184 (2009)
Economou, G.-P.K., Mariatos, E., Economopoulos, N., Lymberopoulos, D., Goutis, C.: FPGA Implementation of Artificial Neural Networks: An Application on Medical Expert Systems. In: 4th Int. Conf. on Microelectronics for Neural Networks and Fuzzy Systems, pp. 287–293 (1994)
Orr, R.: Use of a Probabilistic Neural Network to Estimate the Risk of Mortality after Cardiac Surgery. J. Medical Decision Making 17, 178–185 (1997)
Anagnostou, T., Remzi, M., Djavan, B.: Artificial Neural Networks for Decision-Making in Urologic Oncology. Reviews in Urology 5, 15–21 (2003)
Lemineur, G., Harba, R., Kilic, N., Ucan, O., Osman, O., Benhamou, L.: Efficient Estimation of Osteoporosis Using Artificial Neural Networks. In: 33rd Annual Conf. of IEEE Industrial Electronics Society (IECON), pp. 3039–3044 (2007)
Chiu, J., Li, Y., Yu, F., Wang, Y.: Applying an Artificial Neural Network to Predict Osteoporosis in the Elderly. Studies in Health Technology and Informatics 124, 609–614 (2006)
Mohamed, E., Maiolo, C., Linder, R., Pöppl, S., De Lorenzo, A.: Artificial Neural Network Analysis: A Novel Application For Predicting Site-Specific Bone Mineral Density. Acta Diabetologica 40, 19–22 (2003)
Rae, S., Wang, W., Partridge, D.: Artificial Neural Networks: A Potential Role in Osteoporosis. J. of the Royal Society of Medicine 92, 119–122 (1999)
Anastassopoulos, G., Kolovou, L., Lymperopoulos, D.: A Spatial Distributed Approach for Electronic Medical Record Administration. In: Recent Advances in Communications and Computer Science. Electrical and Computer Engineering Series, A series of Reference Books and Textbooks, WSEAS, pp. 407–412 (2003)
Iliadis, L.: Intelligent Information Systems and applications in risk estimation. Stamoulis Publishing, Thessaloniki (2007)
Gray, R.: Vector Quantization. IEEE Acoustic, Speech, and Signal Processing Magazine 1, 4–29 (1984)
Streiner, D., Cairney, J.: What’s Under the ROC? An Introduction to Receiver Operating Characteristics Curves. The Canadian Journal of Psychiatry 52, 121–128 (2007)
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Mantzaris, D., Anastassopoulos, G., Iliadis, L., Kazakos, K., Papadopoulos, H. (2010). A Soft Computing Approach for Osteoporosis Risk Factor Estimation. In: Papadopoulos, H., Andreou, A.S., Bramer, M. (eds) Artificial Intelligence Applications and Innovations. AIAI 2010. IFIP Advances in Information and Communication Technology, vol 339. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16239-8_18
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DOI: https://doi.org/10.1007/978-3-642-16239-8_18
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