Abstract
This study proposes an Artificial Neural Network (ANN) and Genetic Algorithm model for diagnostic risk factors selection in medicine. A medical disease prediction may be viewed as a pattern classification problem based on a set of clinical and laboratory parameters. Probabilistic Neural Networks (PNNs) were used to face a medical disease prediction. Genetic Algorithm (GA) was used for pruning the PNN. The implemented GA searched for optimal subset of factors that fed the PNN to minimize the number of neurons in the ANN input layer and the Mean Square Error (MSE) of the trained ANN at the testing phase. Moreover, the available data was processed with Receiver Operating Characteristic (ROC) analysis to assess the contribution of each factor to medical diagnosis prediction. The obtained results of the proposed model are in accordance with the ROC analysis, so a number of diagnostic factors in patient's record can be omitted, without any loss in clinical assessment validity.
Chapter PDF
Similar content being viewed by others
Keywords
- Artificial Neural Network
- Mean Square Error
- Receiver Operating Characteristic Analysis
- Area Under Curve
- Probabilistic Neural Network
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Dayhoff J., and DeLeo J., “Artificial Neural Networks Opening the Black Box”, CANCER Supplement, 2001, Vol. 91, No. 8, pp. 1615–1635.
Huang D., “A Constructive Approach for Finding Arbitrary Roots of Polynomials by Neural Networks”, IEEE Transactions on Neural Networks, 2004, Vol. 15, No. 2, pp. 477–491.
Levano M., and Nowak H., “Application of New Algorithm, Iterative SOM, to the Detection of Gene Expressions”, Proceedings 10th International Conference on Engineering Applications of Neural Networks, 2007, Thessaloniki, Greece, pp. 141–147.
Zaknick A., “Introduction to the Modified Probabilistic Neural Network for General Signal Processing”, IEEE Transactions on Signal Processing, 1998, Vol. 46, No.7, pp. 1980–1990.
Iliadis L., “An Intelligent Artificial Neural Network Evaluation System Using Fuzzy Set Hedges: Application in Wood Industry” Proceedings 19th IEEE Annual International Conference on Tools with Artificial Intelligence (ICTA), pp. 366–370.
Paschalidou A., Iliadis L., Kassomenos P., and Bezirtzoglou C., “Neural Modelling of the Tropospheric Ozone Concentrations in an Urban Site”, Proc. 10th International Conference on Engineering Applications of Neural Networks, 2007 Thessaloniki, Greece, pp. 436–445.
Keogan M., Lo J., Freed K., Raptopoulos V., Blake S., Kamel I., Weisinger K., Rosen M., and Nelson R., “Outcome Analysis of Patients with Acute Pancreatitis by Using an Artificial Neural Network”, Academic Radiology, 2002, Vol. 9, No. 4, pp. 410–419.
Brause R., Hanisch E., Paetz J., and Arlt B., “Neural Networks for Sepsis Prediction — the MEDAN-Project1”, Journal für Anästhesie und Intensivbehandlung, 2004, Vol. 11, No. 1, pp. 40–43.
Gómez-Ruiz J., Jerez-Aragonés J., Muñoz-Pérez J., and Alba-Conejo E., “A Neural Network Based Model for Prognosis of Early Breast Cancer”, Applied Intelligence, 2004, Vol. 20, No. 3, pp. 231–238.
Mantzaris D., Anastassopoulos G., Tsalkidis A., and Adamopoulos A., “Intelligent Prediction of Vesicoureteral Reflux Disease”, WSEAS Transactions on Systems, 2005, Vol. 4, Issue 9, pp. 1440–1449.
Anagnostou T., Remzi M., and Djavan B., “Artificial Neural Networks for Decision-Making in Urologic Oncology”, Reviews In Urology, 2003, Vol. 5, No. 1, pp.15–21.
Mantzaris D., Anastassopoulos G., Adamopoulos A., and Gardikis S., “A Non-Symbolic Implementation of Abdominal Pain Estimation in Childhood”, Information Science, 2008, Vol. 178, pp. 3860–3866.
Economou G., Lymperopoulos D., Karavatselou E., and Chassomeris C., “A New Concept Toward Computer-Aided Medical Diagnosis — A Prototype Implementation Addressing Pulmonary Diseases” IEEE Transactions in Information Technology in Biomedicine, 2001 Vol. 5, Issue 1, pp. 55–66.
Mantzaris D., Anastassopoulos C., and Lymperopoulos K., “Medical Disease Prediction Using Artificial Neural Networks”, Proceedings IEEE International Conference on BioInformat-ics and BioEngineering, 2008, Athens, Greece
Georgopoulos E., Likothanassis S., and Adamopoulos A., “Evolving Artificial Neural Networks Using Genetic Algorithms”, Neural Network World, 2000, Vol. 4, pp.565–574
Blazadonakis M., Moustakis V., and Charissis G., “Deep Assessment of Machine Learning Techniques Using Patient Treatment in Acute Abdominal Pain in Children”, Artificial Intelligence in Medicine, 1996, Vol. 8, pp. 527–542
Branke J., “Evolutionary Algorithms for Neural Network Design and Training”, Proceedings 1st Nordic Workshop on Genetic Algorithms and its Applications, 1995, Vaasa, Finland.
Yao X., “Evolving Artificial Neural Networks”, Proceedings of the IEEE, 1999, Vol. 87, No. 9, pp. 1423–1447.
Burgess N., “A Constructive Algorithm that Converges for Real-Valued Input Patterns”, International Journal on Neural Systems, 1994 Vol. 5, No. 1, pp. 59–66.
Angeline P., Sauders G., and Pollack J., “An Evolutionary Algorithm that Constructs Recurrent Neural Networks”, IEEE Transaction on Neural Networks, 1994 Vol. 5, pp. 54–65.
Adamopoulos A., Georgopoulos E., Manioudakis G., and Likothanassis S., “An Evolutionary Method for System Structure Identification Using Neural Networks”, Neural Computation '98, 1998
Billings S., and Zheng G., “Radial Basis Function Network Configuration Using Genetic Algorithms”, Neural Networks, 1995, Vol. 8, pp. 877–890.
Swets J., “Signal Detection Theory and Roc Analysis in Psychology and Diagnostics: Collected Papers”, Lawrence Erlbaum Associates, 1996, Mahwah NJ.
Streiner D., and Cairney J., “What's Under the ROC? An Introduction to Receiver Operating Characteristics Curves”, The Canadian Journal of Psychiatry, 2007, Vol. 52, No. 2, pp. 121– 128.
Parzen E., “On Estimation of a Probability Density Function and Mode”, Annals of Mathematical Statistics, 1962, Vol. 33, No.3, pp. 1065–1076.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 IFIP International Federation for Information Processing
About this paper
Cite this paper
Mantzaris, D., Anastassopoulos, G., Iliadis, Adamopoulos, A. (2009). An Evolutionary Technique for Medical Diagnostic Risk Factors Selection. In: Iliadis, Maglogiann, Tsoumakasis, Vlahavas, Bramer (eds) Artificial Intelligence Applications and Innovations III. AIAI 2009. IFIP International Federation for Information Processing, vol 296. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-0221-4_24
Download citation
DOI: https://doi.org/10.1007/978-1-4419-0221-4_24
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4419-0220-7
Online ISBN: 978-1-4419-0221-4
eBook Packages: Computer ScienceComputer Science (R0)