Extracting a Linguistic Summary from a Medical Database

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Djazia AMGHAR 1,* Amine.M.CHIKH 1

1. Department of Computer Science, Biomedical Engineering Laboratory, University Abou Bekr Belkaid – Tlemcen, B.P.230- Tlemcen 13000, Algérie

* Corresponding author.

DOI: https://doi.org/10.5815/ijisa.2018.12.02

Received: 4 Jun. 2017 / Revised: 5 Jan. 2018 / Accepted: 9 Feb. 2018 / Published: 8 Dec. 2018

Index Terms

Medical data, summary linguistic, fuzzy queries, Medical Data classification, fuzzy logic


In general, medical clustering concerns a big database. The present paper aims at extracting a fuzzy linguistic summary from a large medical database. A linguistic summary is used to reduce large volumes of data to simple sentences. It is worth noting that with the increase of the amount of medical data, different techniques of machine learning have been developed recently.
In this article, an attempt is made to build a medical linguistic summary template. Our linguistic summary model is based on the calculated fuzzy cardinality. It deals with semantic queries in natural language.
Our proposal is to develop a classification system based on the linguistic summary of two medical databases in which the calculation of similarity between different sets of linguistic summaries is used; the patient’s class is then identified by calculating the Sugeno integral.
The present study was successful in developing a classification system that is based on the linguistic summary of two datasets from the UCI Machine Learning Repository, i.e. Pima Indians
Diabetes dataset and Wisconsin Diagnostic Breast Cancer (WDBC) dataset. The results obtained were then employed for a benchmark test.

Cite This Paper

Djazia AMGHAR, Amine.M.CHIKH, "Extracting a Linguistic Summary from a Medical Database", International Journal of Intelligent Systems and Applications(IJISA), Vol.10, No.12, pp.16-26, 2018. DOI:10.5815/ijisa.2018.12.02


[1]J. Nin, P. Salle, S. Bringay, and M. Teisseire, “Using owa operators for gene sequential pattern clustering,” in Computer-Based Medical Systems, 2009. CBMS 2009. 22nd IEEE International Symposium on, 2009, pp. 1–4.
[2]S. T. Rosenbloom, R. A. Miller, K. B. Johnson, P. L. Elkin, and S. H. Brown, “Interface terminologies facilitating direct entry of clinical data into electronic health record systems,” J. Am. Med. Informatics Assoc., vol. 13, no. 3, pp. 277–288, 2006.
[3]L. Liétard, “A functional interpretation of linguistic summaries of data,” Inf. Sci. (Ny)., vol. 188, pp. 1–16, 2012.
[4]P. Bosc and O. Pivert, “SQLf: a relational database language for fuzzy querying,” Fuzzy Syst. IEEE Trans., vol. 3, no. 1, pp. 1–17, 1995.
[5]D. Dubois and H. Prade, “On data summarization with fuzzy sets,” fifth IFSA World Congr., vol. 1, pp. 465–468, 1993.
[6]M. Ros, M. Pegalajar, M. Delgado, A. Vila, D. T. Anderson, J. M. Keller, and M. Popescu, “Linguistic summarization of long-term trends for understanding change in human behavior,” in Fuzzy Systems (FUZZ), 2011 IEEE International Conference on, 2011, pp. 2080–2087.
[7]R. J. Almeida, M.-J. Lesot, B. Bouchon-Meunier, U. Kaymak, and G. Moyse, “Linguistic summaries of categorical time series for septic shock patient data,” in Fuzzy Systems (FUZZ), 2013 IEEE International Conference on, 2013, pp. 1–8.
[8]A. Wilbik, J. M. Keller, and G. L. Alexander, “Linguistic summarization of sensor data for eldercare,” in Systems, Man, and Cybernetics (SMC), 2011 IEEE International Conference on, 2011, pp. 2595–2599.
[9]L. A. Zadeh, “Fuzzy sets,” Inf. Control, vol. 8, no. 3, pp. 338–353, 1965.
[10]L. A. Zadeh, “A computational approach to fuzzy quantifiers in natural languages,” Comput. Math. with Appl., vol. 9, no. 1, pp. 149–184, 1983.
[11]W. A. Voglozin, G. Raschia, L. Ughetto, and N. Mouaddib, “Querying the saintetiq summaries-dealing with null answers,” in Fuzzy Systems, 2005. FUZZ’05. The 14th IEEE International Conference on, 2005, pp. 585–590.
[12]L. Ughetto, W. A. Voglozin, and N. Mouaddib, “Database querying with personalized vocabulary using data summaries,” Fuzzy Sets Syst., vol. 159, no. 15, pp. 2030–2046, 2008.
[13]R. R. Yager, “A new approach to the summarization of data,” Inf. Sci. (Ny)., vol. 28, no. 1, pp. 69–86, 1982.
[14]D. Rasmussen and R. R. Yager, “A fuzzy SQL summary language for data discovery,” Fuzzy Inf. Eng. A Guid. tour Appl., pp. 253–264, 1997.
[15]J. Chomicki, “Preference formulas in relational queries,” ACM Trans. Database Syst., vol. 28, no. 4, pp. 427–466, 2003.
[16]O. Pivert, A. Hadjali, and G. Smits, “Estimating the relevance of a data source using a fuzzy-cardinality-based summary,” in Intelligent Systems (IS), 2010 5th IEEE International Conference, 2010, pp. 96–101.
[17]R. D’Ambrosio, “Handling imbalanced datasets by reconstruction rules in decomposition schemes,” Universit{é} Nice Sophia Antipolis; Universit{à} Campus Bio-Medico di Roma, 2014.
[18]A. Wilbik and J. M. Keller, “A fuzzy measure similarity between sets of linguistic summaries,” Fuzzy Syst. IEEE Trans., vol. 21, no. 1, pp. 183–189, 2013.
[19]A. Wilbik, J. M. Keller, and G. L. Alexander, “Similarity evaluation of sets of linguistic summaries,” Int. J. Intell. Syst., vol. 27, no. 10, pp. 926–938, 2012.
[20]A. Wilbik and J. M. Keller, “A distance metric for a space of linguistic summaries,” Fuzzy Sets Syst., vol. 208, pp. 79–94, 2012.
[21]K. Bache and M. Lichman, “UCI machine learning repository (http://archive. ics. uci. edu/ml). University of California, School of Information and Computer Science,” Irvine, CA, 2013.
[22]M. Sekkal and M. A. Chikh, “NEURO�GENETIC APPROACH TO CLASSIFICATION OF CARDIAC ARRYTHMIAS,” J. Mech. Med. Biol., vol. 12, no. 01, p. 1250010, 2012.
[23]I. Nedjar, M. EL HABIB DAHO, N. Settouti, S. Mahmoudi, and M. A. Chikh, “RANDOM FOREST BASED CLASSIFICATION OF MEDICAL X-RAY IMAGES USING A GENETIC ALGORITHM FOR FEATURE SELECTION,” J. Mech. Med. Biol., vol. 15, no. 02, p. 1540025, 2015.
[24]R. Periyasamy, D. Joshi, S. Atreya, and S. Anand, “LDA aided threshold to classify neuropathy and non neuropathy in diabetic patients,” Int. J. Biomed. Eng. Technol., vol. 7, no. 4, pp. 315–326, 2011.
[25]J. X. Huang, A. An, and Q. Hu, “Medical search and classification tools for recommendation,” in Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval, 2010, p. 707.
[26]K. M. Al-Aidaroos, A. A. Bakar, and Z. Othman, “Medical Data Classification with Naive Bayes Approach.pdf,” Information Technology Journal, vol. 11, no. 9. pp. 1166–1174, 2012.
[27]Y. Bodyanskiy, O. Vynokurova, V. Savvo, T. Tverdokhlib, and P. Mulesa, “Hybrid clustering-classification neural network in the medical diagnostics of reactive arthritis,” arXiv Prepr. arXiv1610.07857, 2016.
[28]A. Goshvarpour, H. Ebrahimnezhad, and A. Goshvarpour, “Classification of epileptic EEG signals using time-delay neural networks and probabilistic neural networks,” Int. J. Inf. Eng. Electron. Bus., vol. 5, no. 1, p. 59, 2013.
[29]J. M. Education, C. Science, S. Lanka, and S. Lanka, “The Effect of Evolutionary Algorithm in Gene Subset Selection for Cancer Classification,” J. Mod. Educ. Comput. Sci., no. July, pp. 60–66, 2018.
[30]R. Saravana, “Medical Big Data Classification Using a Combination of Random Forest Classifier and K- Means Clustering,” I.J. Intell. Syst. Appl., no. November, pp. 11–19, 2018.
[31]M. Delgado, D. Sánchez, and M. A. Vila, “Fuzzy cardinality based evaluation of quantified sentences,” Int. J. Approx. Reason., vol. 23, no. 1, pp. 23–66, 2000.