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Intelligent Decision Support Systems in Automated Medical Diagnosis

  • Florin GorunescuEmail author
  • Smaranda Belciug
Chapter
Part of the Intelligent Systems Reference Library book series (ISRL, volume 137)

Abstract

The Intelligent Decision Support Systems (IDSSs) represent an interdisciplinary research domain bringing together Artificial Intelligence/Machine Learning (AI/ML), Decision Science (DS), and Information Systems (IS). IDSS refers to the use of AI/ML techniques in decision support systems. In this context, it should be emphasized the special role of statistical learning (SL) in the process of training algorithms from data. The purpose of this chapter is to provide a short review of some of the state-of-the-art AI/ML algorithms, seen as intelligent tools used in the medical decision-making, along with some important applications in the automated medical diagnosis of some major chronic diseases (MCDs). In addition, we aim to present an interesting approach to develop novel IDSS inspired by the evolutionary paradigm.

Keywords

Intelligent decision support system Neural networks Support vector machines Evolutionary computation Computer-aided medical diagnosis 

References

  1. 1.
    Gorunescu, F.: Data Mining Concepts, Models and Techniques. Springer-Verlag, Berlin Heidelberg (2011)zbMATHGoogle Scholar
  2. 2.
    Pang, S., Yu, Z., Orgun, M.A.: A novel end-to-end classifier using domain transferred deep convolutional neural networks for biomedical images. Comput Methods programs. Biomed. (2017). Epub 2017 Jan 6. doi: 10.1016/j.cmpb.2016.12.019
  3. 3.
    Hatipoglu, N., Bilgin, G.: Cell segmentation in histopathological images with deep learning algorithms by utilizing spatial relationships. Med Biol Eng Comput. [Epub ahead of print] (2017). doi: 10.1007/s11517-017-1630-1
  4. 4.
    Arabasadi, Z., Alizadehsani, R., Roshanzamir, M., Moosaei, H., Yarifard, A.A.: Computer aided decision making for heart disease detection using hybrid neural network-Genetic algorithm. Comput Methods programs Biomed. Epub 2017 Jan 18 (2017). doi: 10.1016/j.cmpb.2017.01.004
  5. 5.
    Ma, J., Yu, J., Hao, G., Wang, D., Sun, Y., lu, J., Cao, H., Lin, F.: Assessment of triglyceride and cholesterol in overweight people based on multiple linear regression and artificial intelligence model. lipids health Dis. (2017). doi: 10.1186/s12944-017-0434-5
  6. 6.
    Becker, A.S., Marcon, M., Ghafoor, S., Wurnig, M.C., Frauenfelder, T, Boss, A.: Deep Learning in Mammography: Diagnostic Accuracy of a Multipurpose Image Analysis Software in the Detection of Breast Cancer. Invest Radiol. [Epub ahead of print] (2017). doi: 10.1097/RLI.0000000000000358
  7. 7.
    Huang, M.W., Chen, C.W., Lin, W.C., Shih-Wen Ke, S.W., Tsai C.F.: SVM and SVM Ensembles in Breast Cancer Prediction. PLoS One 12(1), (2017). doi: 10.1371/journal.pone.0161501
  8. 8.
    Anaissi, A., Goyal, M., Catchpoole, D., Braytee, A., Kennedy, P.: Ensemble Feature Learning of Genomic Data Using Support Vector Machine. PLoS One 11(6), doi: 10.1371/journal.pone.0157330 (2016)
  9. 9.
    Huiyan Jiang, H., Zhao, D., Zheng, R., Ma, X.: Construction of Pancreatic Cancer Classifier Based on SVM Optimized by Improved FOA. Biomed Res Int. (2015). doi: 10.1155/2015/781023
  10. 10.
    Fu, C.W., Lin, T.H.: Predicting the Metabolic Sites by Flavin-Containing Monooxygenase on Drug Molecules Using SVM Classification on Computed Quantum Mechanics and Circular Fingerprints Molecular Descriptors. PLoS One 12(1), (2017). doi: 10.1371/journal.pone.0169910
  11. 11.
    Weis, C., Hess, A., Budinsky, L., Fabry, B.: In-Vivo Imaging of Cell Migration Using Contrast Enhanced MRI and SVM Based Post-Processing. PLoS One 10(12), (2015). doi: 10.1371/journal.pone.0140548
  12. 12.
    Banerjee, U., Braga-Neto, U.M.: Bayesian ABC-MCMC Classification of Liquid Chromatography-Mass Spectrometry Data. Cancer Inform 14(5), 175–182 (2017)Google Scholar
  13. 13.
    Jung, Y.J., Katilius, E., Ostroff, R.M., Kim, Y., Seok, M., Lee, S., Jang, S., Kim, W.S., Choi, C.M.: Development of a Protein Biomarker Panel to Detect Non-Small-Cell Lung Cancer in Korea. Clin Lung Cancer. [Epub ahead of print] (2016). doi: 10.1016/j.cllc.2016.09.012
  14. 14.
    Benndorf, M., Neubauer, J., Langer, M., Kotter, E.: Bayesian pretest probability estimation for primary malignant bone tumors based on the Surveillance, Epidemiology and End Results Program (SEER) database. Int. J. Comput. Assist. Radiol. Surg. 12(3), 485–491 (2017)CrossRefGoogle Scholar
  15. 15.
    Wang, J., Zuo, Y., Man, Y., Tadesse, M.G., Ressom, H.W.: Identification of functional modules by integration of multiple data sources using a Bayesian network classifier. Circ Cardiovasc Genet 7(2), 206–217 (2014)CrossRefGoogle Scholar
  16. 16.
    Ricci, L., Del Vescovo, V., Cantaloni, C., Grasso, M., Barbareschi, M., Denti, M.A.: Statistical analysis of a Bayesian classifier based on the expression of miRNAs. BMC Bioinformatics (2015). doi: 10.1186/s12859-015-0715-9
  17. 17.
    Sreekumari, A., Shriram, K.S., Vaidya, V.: Breast lesion detection and characterization with 3D features. Proc IEEE Conf Eng Med Biol Soc., pp. 4101–4104 (2016)Google Scholar
  18. 18.
    Yu, K., Sang, Q.A., Lung, P.Y., Tan, W., Lively, T., Sheffield, C., Bou-Dargham, M.J., Liu, J.S., Zhang, J.: Personalized chemotherapy selection for breast cancer using gene expression profiles. Sci Rep. (2017) doi: 10.1038/srep43294
  19. 19.
    Kotti, M., Duffell, L.D., Faisal, A.A., McGregor, A.H.: Detecting knee osteoarthritis and its discriminating parameters using random forests. Med Eng Phys. [Epub ahead of print] (2017) doi: 10.1016/j.medengphy2017.02.004
  20. 20.
    Adham, D., Abbasgholizadeh, N., Abazari, M.: Prognostic Factors for Survival in Patients with Gastric Cancer using a Random Survival Forest. Asian Pac. J. Cancer Prev 18(1), 129–134 (2017)Google Scholar
  21. 21.
    Paul, D., Su, R., Romain, M., Sébastien, V., Pierre, V., Isabelle, G.: Feature selection for outcome prediction in oesophageal cancer using genetic algorithm and random forest classifier. Comput Med Imaging Graph. [Epub ahead of print] (2016). doi: 10.1016/j.compmedimag.2016.12.002
  22. 22.
    Gorunescu, F., Belciug, S.: Boosting backpropagation algorithm by stimulus-sampling: Application in computer-aided medical diagnosis. J. Biomed. Inform. 63, 74–81 (2016)CrossRefGoogle Scholar
  23. 23.
    Belciug, S., Gorunescu, F.: Error-correction learning for artificial neural networks using the Bayesian paradigm. Application to automated medical diagnosis. J. Biomed. Inform. 52, 329–337 (2014)CrossRefGoogle Scholar
  24. 24.
    Belciug, S., Gorunescu, F.: A hybrid neural network/genetic algorithm system applied to the breast cancer detection and recurrence. Expert Systems 30(3), 243–254 (2013)CrossRefGoogle Scholar
  25. 25.
    Belciug, S., El-Darzi E.: A partially connected neural network-based approach with application to breast cancer detection and recurrence. In: Proc. 5th IEEE conference on intelligent systems-IS, 7–9 July 2010, London, UK. pp. 191–196 (2010)Google Scholar
  26. 26.
    Gorunescu, F., Belciug, S., Gorunescu, M., Badea, R.: Intelligent decision-making for liver fibrosis stadialization based on tandem feature selection and evolutionary-driven neural network. Expert Syst. Appl. 39(17), 12824–12832 (2012)CrossRefGoogle Scholar
  27. 27.
    Saftoiu, A., Vilmann, P., Gorunescu, F., et al.: Efficacy of an Artificial Neural Network-Based Approach to Endoscopic Ultrasound Elastography in Diagnosis of Focal Pancreatic Masses. Clinical Gastroent and Hepatol 10(1), 84–90 (2012)CrossRefGoogle Scholar
  28. 28.
    Gorunescu, F., Belciug, S., Gorunescu, M., Lupsor, M., Badea R., Ştefanescu, H.: Radial basis function network-based diagnosis for liver fibrosis estimation. In Proc. 2nd International Conference on e-Health and Bioengineering-EHB 2009, 17–18th September, 2009, Iaşi-Constanţa, Romania, Ed. UMF “Gr.T. Popa” Iasi. pp. 209–212 (2009)Google Scholar
  29. 29.
    Belciug, S., Gorunescu, F., Gorunescu M., Salem A.B.: Assessing Performances of Unsupervised and Supervised Neural Networks in Breast Cancer Detection. In: Proc. 7th IEEE International Conference on INFOrmatics and Systems-INFOS 2010. Advances in Data Engineering and Management-ADEM, March, 28–30, 2010, Cairo, pp. 80–87 (2010)Google Scholar
  30. 30.
    Belciug, S., Gorunescu, F., Gorunescu, M., Salem, A.B.: Clustering-based approach for detecting breast cancer recurrence. In: Proc. 10th IEEE International Conference on Intelligent Systems Design and Applications-ISDA10, Nov 29 – Dec 1, 2010, Cairo, pp. 533–538 (2010)Google Scholar
  31. 31.
    Stoean, C. Stoean, R.: Support vector machines and evolutionary algorithms for classification. Springer (2014)Google Scholar
  32. 32.
    Stoean, C., Stoean, R.: Evolution of Cooperating Classification Rules with an Archiving Strategy to Underpin Collaboration. Springer (Evolution of Cooperating Classification Rules with an Archiving Strategy to Underpin Collaboration, Intelligent Systems and Technologies- Methods and Applications), pp. 47–65 (2009)Google Scholar
  33. 33.
    Stoean, C., Stoean, R., Lupsor, M., Stefanescu, H., Badea, R.: Feature Selection for a Cooperative Coevolutionary Classifier in Liver Fibrosis Diagnosis. Comput. Biol. Med. 41(4), 238–246 (2011)CrossRefzbMATHGoogle Scholar
  34. 34.
    Stoean, R., Stoean, C., Sandita, A., Ciobanu, D., Mesina, C.: Interpreting Decision Support from Multiple Classifiers for Predicting Length of Stay in Patients with Colorectal Carcinoma, Neural Processing Letters, pp. 1–17, (2017). doi: 10.1007/s11063-017-9585-7
  35. 35.
    Stoean, C., Stoean, R.: Post-evolution of variable-length class prototypes to unlock decision making within support vector machines. Appl. Soft Comput. 25, 159–173 (2014)CrossRefzbMATHGoogle Scholar
  36. 36.
    Gorunescu, F., Gorunescu, M., Saftoiu, A., Vilmann, P., Belciug, S.: Competitive/ collaborative neural computing system for medical diagnosis in pancreatic cancer detection. Expert Syst 28(1), 33–44 (2011)CrossRefGoogle Scholar
  37. 37.
    Stoean, R., Stoean, C., Sandita, A., Ciobanu, D., Mesina, C.: Ensemble of Classifiers for Length of Stay Prediction in Colorectal Cancer. International Work-Conference on Artificial Neural Networks (IWANN 2015), Advances in Computational Intelligence, Lecture Notes in Computer Science, Springer, Volume 9094, Palma de Mallorca, Spain, 10–12 June, pp. 444–457 (2015)Google Scholar
  38. 38.
    Stoean, C., Stoean, R., Sandita, A.: Investigation of Alternative Evolutionary Prototype Generation in Medical Classification. In: IEEE Post-Proc. 16th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), September 22 – 25, 2014, Timisoara, Romania, pp. 537–543 (2014)Google Scholar
  39. 39.
    Gorunescu, F.: Intelligent decision systems in Medicine -a short survey on medical diagnosis and patient management (keynote speech). In: Proc. 5th IEEE International Conference on “E-Health and Bioengineering”-EHB 2015, 19–21 November 2015, Iasi, Romania, pp. 1-8 (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Chair of Mathematics, Biostatistics and Computer ScienceUniversity of Medicine and Pharmacy of CraiovaCraiovaRomania
  2. 2.Department of Computer Science, Faculty of SciencesUniversity of CraiovaCraiovaRomania

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