Improving Retrieval Performance of Case Based Reasoning Systems by Fuzzy Clustering.

F. Saadi; B. Atmani; F. Henni

doi:10.9781/ijimai.2023.07.002

Authors

F. Saadi Université Oran 1 Ahmed Ben Bella
B. Atmani Université de Mostaganem
F. Henni Université de Mostaganem

DOI:

https://doi.org/10.9781/ijimai.2023.07.002

Keywords:

Case Based Reasoning, Case Retrieval, Classification, Data Mining, Decision Support System, Fuzzy Logic, Disease-Modifying Therapy (DMT), Kmeans

Abstract

Case-based reasoning (CBR), which is a classical reasoning methodology, has been put to use. Its application has allowed significant progress in resolving problems related to the diagnosis, therapy, and prediction of diseases. However, this methodology has shown some complicated problems that must be resolved, including determining a representation form for the case (complexity, uncertainty, and vagueness of medical information), preventing the case base from the infinite growth of generated medical information and selecting the best retrieval technique. These limitations have pushed researchers to think about other ways of solving problems, and we are recently witnessing the integration of CBR with other techniques such as data mining. In this article, we develop a new approach integrating clustering (Fuzzy C-Means (FCM) and K-Means) in the CBR cycle. Clustering is one of the crucial challenges and has been successfully used in many areas to develop innate structures and hidden patterns for data grouping [1]. The objective of the proposed approach is to solve the limitations of CBR and improve it, particularly in the search for similar cases (retrieval step). The approach is tested with the publicly available immunotherapy dataset. The results of the experimentations show that the integration of the FCM algorithm in the retrieval step reduces the search space (the large volume of information), resolves the problem of the vagueness of medical information, speeds up the calculation and response time, and increases the search efficiency, which further improves the performance of the retrieval step and, consequently, the CBR system.

Downloads

Download data is not yet available.

References

A. Seal, E. Herrera Viedma, et al., “Performance and convergence analysis of modified c-means using jeffreys-divergence for clustering,” International Journal of Interactive Multimedia and Artificial Intelligence, vol. 7, no. 2, pp. 141–149, 2021, doi: https://doi.org/10.9781/ijimai.2021.04.009.

A. Aamodt, E. Plaza, “Case-based reasoning: Foundational issues, methodological variations, and system approaches,” AI communications, vol. 7, no. 1, pp. 39–59, 1994.

M. Pantic, “Introduction to machine learning & case- based reasoning,” London: Imperial College, 2005.

N. Choudhury, S. A. Begum, “A survey on case- based reasoning in medicine,” International Journal of Advanced Computer Science and Applications, vol. 7, no. 8, 2016.

S. Montani, “Exploring new roles for case-based reasoning in heterogeneous ai systems for medical decision support,” Applied Intelligence, vol. 28, no. 3, pp. 275–285, 2008.

V. E. Ekong, U. G. Inyang, E. A. Onibere, “Intelligent decision support system for depression diagnosis based on neuro-fuzzy-cbr hybrid,” Modern Applied Science, vol. 6, no. 7, p. 79, 2012.

A. Mansoul, B. Atmani, “Clustering to enhance case- based reasoning,” in Modelling and Implementation of Complex Systems, Springer, 2016, pp. 137–151.

R. Schmidt, L. Gierl, “Prognostic model for early warning of threatening influenza waves,” in 1st German workshop on experience management: sharing experiences about the sharing of experience, 2002, Gesellschaft für Informatik eV.

I. Bichindaritz, C. Marling, “Case-based reasoning in the health sciences: Foundations and research directions,” in Computational Intelligence in Healthcare 4, Springer, 2010, pp. 127–157.

M. Abdelhak, A. Baghdad, “Combining multi-criteria analysis with cbrfor medical decision support,” Journal of Information Processing Systems, vol. 13, no. 6, pp. 1496– 1515, 2017.

F. Saadi, B. Atmani, F. Henni, “Integration of fuzzy clustering into the case base reasoning for the prediction of response to immunotherapy treatment,” in Mediterranean Conference on Pattern Recognition and Artificial Intelligence, 2019, pp. 192–206, Springer.

M. Benamina, B. Atmani, S. Benbelkacem, “Diabetes diagnosis by case-based reasoning and fuzzy logic,” International Journal of Interactive Multimedia and Artificial Intelligence, vol. 5, no. 3, pp. 72–80, 2018.

S. Sharma, D. Mehrotra, “Building cbr based diagnosis system using jcolibri,” in 2017 7th International Conference on Cloud Computing, Data Science & Engineering-Confluence, 2017, pp. 634–638, IEEE.

S. Demigha, “A generic elearning tool for radiologists and hospital practitioners with cbr,” in European Conference on e-Learning, 2015, p. 809, Academic Conferences International Limited.

D. Gu, C. Liang, H. Zhao, “A case-based reasoning system based on weighted heterogeneous value distance metric for breast cancer diagnosis,” Artificial intelligence in medicine, vol. 77, pp. 31–47, 2017.

H. Benfriha, B. Atmani, B. Khemliche, N. T. Aoul, Douah, “A multi-labels text categorization framework for cerebral lesion’s identification,” in International Conference on Computing, 2019, pp. 103– 114, Springer.

S. El-Sappagh, M. M. Elmogy, “Medical case based reasoning frameworks: Current developments and future directions,” Virtual and Mobile Healthcare: Breakthroughs in Research and Practice, pp. 516–552, 2020.

C. Aflori, M. Craus, “Grid implementation of the apriori algorithm,”Advances in engineering software, vol. 38, no. 5, pp. 295–300, 2007.

K. Srinivas, G. R. Rao, A. Govardhan, “Analysis of coronary heart disease and prediction of heart attack in coal mining regions using data mining techniques,” in 2010 5th International Conference on Computer Science & Education, 2010, pp. 1344–1349, IEEE.

A. Dewan, M. Sharma, “Prediction of heart disease using a hybrid technique in data mining classification,” in 2015 2nd International Conference on Computing for Sustainable Global Development (INDIACom), 2015, pp. 704–706, IEEE.

M. Chen, Y. Hao, K. Hwang, L. Wang, L. Wang, “Disease prediction by machine learning over big data from healthcare communities,” Ieee Access, vol. 5, pp. 8869–8879, 2017.

S. Kumar, G. Sahoo, “Classification of heart disease using naive bayes and genetic algorithm,” in Computational Intelligence in Data Mining-Volume 2, Springer, 2015, pp. 269–282.

V. Chaurasia, S. Pal, “Early prediction of heart diseases using data mining techniques,” Caribbean Journal of Science and Technology, vol. 1, pp. 208–217, 2013.

P. R. Hachesu, M. Ahmadi, S. Alizadeh, F. Sadoughi, “Use of data mining techniques to determine and predict length of stay of cardiac patients,” Healthcare informatics research, vol. 19, no. 2, pp. 121–129, 2013.

M. Martín Merino, A. J. López Rivero, V. Alonso, M. Vallejo, A. Ferreras, “A clustering algorithm based on an ensemble of dissimilarities: An application in the bioinformatics domain.,” International Journal of Interactive Multimedia & Artificial Intelligence, vol. 7, no. 6, 2022.

A. Karlekar, A. Seal, O. Krejcar, C. Gonzalo-Martin, “Fuzzy k-means using non-linear s-distance,” IEEE Access, vol. 7, pp. 55121–55131, 2019.

S. Kapil, M. Chawla, “Performance evaluation of k-means clustering algorithm with various distance metrics,” in 2016 IEEE 1st international conference on power electronics, intelligent control and energy systems (ICPEICES), 2016, pp. 1–4, IEEE.

K. K. Sharma, A. Seal, “Clustering analysis using an adaptive fused distance,” Engineering Applications of Artificial Intelligence, vol. 96, p. 103928, 2020.

A. Seal, A. Karlekar, O. Krejcar, C. Gonzalo-Martin, “Fuzzy c-means clustering using jeffreys-divergence based similarity measure,” Applied Soft Computing, vol. 88, p. 106016, 2020.

S. Begum, M. U. Ahmed, S. Barua, “Multi-scale entropy analysis and case-based reasoning to classify physiological sensor signals,” Luc Lamontagne and Juan A. Recio-Garcıa (Editors), vol. 129, 2012.

S. Benbelkacem, B. Atmani, M. Benamina, “Treatment tuberculosis retrieval using decision tree,” in 2013 international conference on control, decision and information technologies (CoDIT), 2013, pp. 283–288, IEEE.

X. Blanco, S. Rodríguez, J. M. Corchado, C. Zato, “Case-based reasoning applied to medical diagnosis and treatment,” in distributed computing and artificial intelligence, Springer, 2013, pp. 137–146.

H. Benfriha, B. Atmani, F. Barigou, F. Henni, B. Khemliche, S. Fatima, A. Douah, Z. Z. Addou, “Improving cbr retrieval process through multilabel text categorization for health care of childhood traumatic brain injuries in road accident,” in Proceedings of Sixth International Congress on Information and Communication Technology, 2022, pp. 721–731, Springer.

D. Gu, W. Zhao, Y. Xie, X. Wang, K. Su, O. V. Zolotarev, “A personalized medical decision support system based on explainable machine learning algorithms and ecc features: Data from the real world,” Diagnostics, vol. 11, no. 9, p. 1677, 2021.

Y.-G. Jung, B. Kim, H. Nam, M. Rhee, J.-S. Lee, “Effective diagnosis of coronary artery disease using case-based reasoning,” Turkish Journal of Computer and Mathematics Education (TURCOMAT), vol. 12, no. 5, pp. 449–457, 2021.

F. Saadi, B. Atmani, F. Henni, “Integration of datamining techniques into the cbr cycle to predict the result of immunotherapy treatment,” in 2019 International Conference on Computer and Information Sciences (ICCIS), 2019, pp. 1–5, IEEE.

G. Khussainova, S. Petrovic, R. Jagannathan, “Retrieval with clustering in a case-based reasoning system for radiotherapy treatment planning,” in Journal of Physics: Conference Series, vol. 616, 2015, p. 012013, IOP Publishing.

C. Koo, W. Li, S. H. Cha, S. Zhang, “A novel estimation approach for the solar radiation potential with its complex spatial pattern via machine-learning techniques,” Renewable energy, vol. 133, pp. 575–592, 2019.

P. Yadav, “Case retrieval algorithm using similarity measure and adaptive fractional brain storm optimization for health informaticians,” Arabian Journal for Science and Engineering, vol. 41, no. 3, pp. 829–840, 2016.

S. Geetha, S. Narayanamoorthy, T. Manirathinam, D. Kang, “Fuzzy case-based reasoning approach for finding covid-19 patients priority in hospitals at source shortage period,” Expert Systems with Applications,vol. 178, p. 114997, 2021.

H. D. Ibrahim, T. O. Odedele, “Covid19 infectious disease detection and diagnosis system using case- based reasoning and fuzzy logic inference model,” in International Conference on Intelligent and Fuzzy Systems, 2021, pp. 162–170, Springer.

N. Choudhury, S. A. Begum, “Neuro-fuzzy-rough classification for improving efficiency and performance in case-based reasoning retrieval,” n Computational Network Application Tools for Performance Management, Springer, 2020, pp. 29–38.

Z. Yamin, Z. Mengmeng, G. Xiaomin, Z. Zhiwei, Z. Jianhua, “Research on matching method for case retrieval process in cbr based on fcm,” Procedia engineering, vol. 174, pp. 267–274, 2017.

S. Banerjee, A. R. Chowdhury, “Case based reasoning in the detection of retinal abnormalities using decision trees,” Procedia Computer Science, vol. 46, pp. 402–408, 2015.

S. R. Garner, et al., “Weka: The waikato environment for knowledge analysis,” in Proceedings of the New Zealand computer science research students conference, vol. 1995, 1995, pp. 57–64.

S. Ray, R. H. Turi, “Determination of number of clusters in k-means clustering and application in colour image segmentation,” in Proceedings of the 4th international conference on advances in pattern recognition and digital techniques, 1999, pp. 137–143, Citeseer.

T. M. Kodinariya, P. R. Makwana, “Review on determining number of cluster in k-means clustering,” International Journal of Advance Research in Computer Science and Management Studies, vol. 1, no. 6, pp. 90–95, 2013.

J. C. Dunn, “A fuzzy relative of the isodata process and its use in detecting compact well-separated clusters,” Journal of Cybernetics, vol. 3, no. 3, pp. 32–57, 1973, doi: 10.1080/01969727308546046.

W. Zhu, N. Zeng, N. Wang, et al., “Sensitivity, specificity, accuracy, associated confidence interval and roc analysis with practical sas implementations,” NESUG proceedings: health care and life sciences, Baltimore, Maryland, vol. 19, p. 67, 2010.

F. Khozeimeh, R. Alizadehsani, M. Roshanzamir, A. Khosravi, P. Layegh, S. Nahavandi, “An expert system for selecting wart treatment method,” Computers in biology and medicine, vol. 81, pp. 167– 175, 2017.