T. Anagnostou, M. Remzi, and B. Djavan, Artificial neural networks for decisionmaking in urologic oncology, Review in Urology, vol.5, issue.1, pp.15-21, 2003.
DOI : 10.1016/s0302-2838(03)00133-7
URL : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1472995/pdf

G. C. Anastassopoulos and L. S. Iliadis, Ann for prognosis of abdominal pain in childhood: Use of fuzzy modelling for convergence estimation, Proceedings of the 1st International Workshop on Combinations of Intelligent Methods and Applications, pp.1-5, 2008.

T. Bellotti, Z. Luo, A. Gammerman, F. W. Delft, and V. Saha, QUALIFIED PREDICTIONS FOR MICROARRAY AND PROTEOMICS PATTERN DIAGNOSTICS WITH CONFIDENCE MACHINES, International Journal of Neural Systems, vol.102, issue.04, pp.247-258, 2005.
DOI : 10.1093/bioinformatics/btg484

M. Elter, R. Schulz-wendtland, and T. Wittenberg, The prediction of breast cancer biopsy outcomes using two CAD approaches that both emphasize an intelligible decision process, Medical Physics, vol.92, issue.11, pp.4164-4172, 2007.
DOI : 10.1007/978-1-4899-4541-9

A. Frank and A. Asuncion, UCI machine learning repository, 2010.

A. Gammerman, V. Vovk, B. Burford, I. Nouretdinov, Z. Luo et al., Serum Proteomic Abnormality Predating Screen Detection of Ovarian Cancer, The Computer Journal, vol.52, issue.3, pp.326-333, 2009.
DOI : 10.1093/comjnl/bxn021

H. Holst, M. Ohlsson, C. Peterson, and L. Edenbrandt, Intelligent computer reporting `lack of experience': a confidence measure for decision support systems, Clinical Physiology, vol.27, issue.2, pp.139-147, 1998.
DOI : 10.1016/0010-4655(94)90120-1

I. Kononenko, Machine learning for medical diagnosis: history, state of the art and perspective, Artificial Intelligence in Medicine, vol.23, issue.1, pp.89-109, 2001.
DOI : 10.1016/S0933-3657(01)00077-X

A. Lambrou, H. Papadopoulos, and A. Gammerman, Reliable Confidence Measures for Medical Diagnosis With Evolutionary Algorithms, IEEE Transactions on Information Technology in Biomedicine, vol.15, issue.1, pp.93-99, 2011.
DOI : 10.1109/TITB.2010.2091144

A. Lambrou, H. Papadopoulos, E. Kyriacou, C. S. Pattichis, M. S. Pattichis et al., Assessment of Stroke Risk Based on Morphological Ultrasound Image Analysis with Conformal Prediction, Proceedings of the 6th IFIP International Conference on Artificial Intelligence Appications and Innovations, pp.146-153, 2010.
DOI : 10.1007/978-3-642-16239-8_21
URL : https://hal.archives-ouvertes.fr/hal-01060662

P. J. Lisboa, A review of evidence of health benefit from artificial neural networks in medical intervention, Neural Networks, vol.15, issue.1, pp.11-39, 2002.
DOI : 10.1016/S0893-6080(01)00111-3

D. Mantzaris, G. Anastassopoulos, L. Iliadis, K. Kazakos, and H. Papadopoulos, A Soft Computing Approach for Osteoporosis Risk Factor Estimation, Proceedings of the 6th IFIP International Conference on Artificial Intelligence Appications and Innovations, pp.120-127, 2010.
DOI : 10.1007/978-3-642-16239-8_18
URL : https://hal.archives-ouvertes.fr/hal-01060659

A. H. Murphy, A New Vector Partition of the Probability Score, Journal of Applied Meteorology, vol.12, issue.4, pp.595-600, 1973.
DOI : 10.1175/1520-0450(1973)012<0595:ANVPOT>2.0.CO;2

I. Nouretdinov, T. Melluish, and V. Vovk, Ridge regression confidence machine, Proceedings of the 18th International Conference on Machine Learning (ICML'01, pp.385-392, 2001.

H. Papadopoulos, Inductive Conformal Prediction: Theory and Application to Neural Networks, Artificial Intelligence InTech, issue.18, pp.315-3305294, 2008.
DOI : 10.5772/6078
URL : http://www.intechopen.com/download/pdf/5294

H. Papadopoulos, A. Gammerman, and V. Vovk, Reliable diagnosis of acute abdominal pain with conformal prediction, Engineering Intelligent Systems, vol.17, issue.2-3, pp.115-126, 2009.

H. Papadopoulos and H. Haralambous, Reliable prediction intervals with regression neural networks, Neural Networks, vol.24, issue.8, 2011.
DOI : 10.1016/j.neunet.2011.05.008

H. Papadopoulos, K. Proedrou, V. Vovk, and A. Gammerman, Inductive Confidence Machines for Regression, Proceedings of the 13th European Conference on Machine Learning (ECML'02). LNCS, pp.345-356, 2002.
DOI : 10.1007/3-540-36755-1_29
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.4.7849

H. Papadopoulos, V. Vovk, and A. Gammerman, Regression conformal prediction with nearest neighbours, Journal of Artificial Intelligence Research, vol.40, pp.815-8403198, 2011.

C. S. Pattichis, C. Christodoulou, E. Kyriacou, and M. S. Pattichis, Artificial neural networks in medical imaging systems, Proceedings of the 1st MEDINF International Conference on Medical Informatics and Engineering, pp.83-91, 2003.

K. Proedrou, I. Nouretdinov, V. Vovk, and A. Gammerman, Transductive Confidence Machines for Pattern Recognition, Proceedings of the 13th European Conference on Machine Learning (ECML'02), pp.381-390, 2002.
DOI : 10.1007/3-540-36755-1_32
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.15.9249

C. Saunders, A. Gammerman, and V. Vovk, Transduction with confidence and credibility, Proceedings of the 16th International Joint Conference on Artificial Intelligence, pp.722-726, 1999.

J. W. Smith, J. E. Everhart, W. C. Dickson, W. C. Knowler, and R. S. Johannes, Using the ADAP learning algorithm to forecast the onset of diabetes mellitus, Proceedings of the Annual Symposium on Computer Applications and Medical Care, pp.261-265, 1988.

V. Vovk, A. Gammerman, and G. Shafer, Algorithmic Learning in a Random World, 2005.