Cluster Data Analysis with a Fuzzy Equivalence Relation to Substantiate a Medical Diagnosis

Abas Hasanovich Lampezhev, Elena Yur`evna Linskaya, Aslan Adal`bievich Tatarkanov, Islam Alexandrovich Alexandrov

Abstract


This study aims to develop a methodology for the justification of medical diagnostic decisions based on the clustering of large volumes of statistical information stored in decision support systems. This aim is relevant since the analyzed medical data are often incomplete and inaccurate, negatively affecting the correctness of medical diagnosis and the subsequent choice of the most effective treatment actions. Clustering is an effective mathematical tool for selecting useful information under conditions of initial data uncertainty. The analysis showed that the most appropriate algorithm to solve the problem is based on fuzzy clustering and fuzzy equivalence relation. The methods of the present study are based on the use of this algorithm forming the technique of analyzing large volumes of medical data due to prepare a rationale for making medical diagnostic decisions. The proposed methodology involves the sequential implementation of the following procedures: preliminary data preparation, selecting the purpose of cluster data analysis, determining the form of results presentation, data normalization, selection of criteria for assessing the quality of the solution, application of fuzzy data clustering, evaluation of the sample, results and their use in further work. Fuzzy clustering quality evaluation criteria include partition coefficient, entropy separation criterion, separation efficiency ratio, and cluster power criterion. The novelty of the results of this article is related to the fact that the proposed methodology makes it possible to work with clusters of arbitrary shape and missing centers, which is impossible when using universal algorithms.

 

Doi: 10.28991/esj-2021-01305

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Keywords


Medical Decision Support System; Fuzzy Logic; Fuzzy Clustering Algorithms; k-means Algorithm; c-means Algorithm.

References


Bricon-Souf, N., C. Verdier, A. Flory, and M.C. Jaulent. “Theme C: Medical Information Systems and Databases – Results and Future Work.” IRBM 34, no. 1 (February 2013): 9–10. doi:10.1016/j.irbm.2012.12.010.

Wagholikar, Kavishwar Balwant, Kathy L MacLaughlin, Thomas M Kastner, Petra M Casey, Michael Henry, Robert A Greenes, Hongfang Liu, and Rajeev Chaudhry. “Formative Evaluation of the Accuracy of a Clinical Decision Support System for Cervical Cancer Screening.” Journal of the American Medical Informatics Association 20, no. 4 (July 2013): 749–757. doi:10.1136/amiajnl-2013-001613.

Piibe, Quinn, Erica Kane, Marlene Melzer-Lange, and Kathleen Beckmann. “Patient at Risk: Emergency Medical Service Providers’ Opinions on Improving an Electronic Emergency Information Form Database for the Medical Care of Children with Special Health Care Needs.” Disability and Health Journal 13, no. 2 (April 2020): 100852. doi:10.1016/j.dhjo.2019.100852.

Andrikov, D.A., and A.S. Kuchin. “Development of a Prototype of a Medical Information System for a Clinical Diagnostic Center.” Procedia Computer Science 186 (2021): 287–292. doi:10.1016/j.procs.2021.04.147.

Chang, Wenjun, Qian Zhang, Chao Fu, Weiyong Liu, Guangquan Zhang, and Jie Lu. “A Cross-Domain Recommender System through Information Transfer for Medical Diagnosis.” Decision Support Systems 143 (April 2021): 113489. doi:10.1016/j.dss.2020.113489.

Anifah, Lilik, and Haryanto. “Decision Support System Two Dimensional Cattle Weight Estimation Using Fuzzy Rule Based System.” 2021 3rd East Indonesia Conference on Computer and Information Technology (EIConCIT) (April 9, 2021). doi:10.1109/eiconcit50028.2021.9431911.

L, Arokia Jesu Prabhu, Sudhakar Sengan, Kamalam G K, Vellingiri J, Jagadeesh Gopal, Priya Velayutham, and Subramaniyaswamy V. “Medical Information Retrieval Systems for e-Health Care Records Using Fuzzy Based Machine Learning Model.” Microprocessors and Microsystems (October 2020): 103344. doi:10.1016/j.micpro.2020.103344.

Zhao, Yan, Li Liu, Yanbo Qi, Fengge Lou, Jingdan Zhang, and Wenhui Ma. “Evaluation and Design of Public Health Information Management System for Primary Health Care Units Based on Medical and Health Information.” Journal of Infection and Public Health 13, no. 4 (April 2020): 491–496. doi:10.1016/j.jiph.2019.11.004.

Tashkandi, Araek, Ingmar Wiese, and Lena Wiese. “Efficient In-Database Patient Similarity Analysis for Personalized Medical Decision Support Systems.” Big Data Research 13 (September 2018): 52–64. doi:10.1016/j.bdr.2018.05.001.

Cibella, Fabio, Simona Panunzi, Valerio Cusimano, and Andrea De Gaetano. “Decision Support for Medical Disasters: Evaluation of the IMPRESS System in the Live Palermo Demo.” International Journal of Disaster Risk Reduction 50 (November 2020): 101695. doi:10.1016/j.ijdrr.2020.101695.

Shaikh, Faiq, Jamshid Dehmeshki, Sotirios Bisdas, Diana Roettger-Dupont, Olga Kubassova, Mehwish Aziz, and Omer Awan. “Artificial Intelligence-Based Clinical Decision Support Systems Using Advanced Medical Imaging and Radiomics.” Current Problems in Diagnostic Radiology 50, no. 2 (March 2021): 262–267. doi:10.1067/j.cpradiol.2020.05.006.

Katzmann, Alexander, Oliver Taubmann, Stephen Ahmad, Alexander Mühlberg, Michael Sühling, and Horst-Michael Groß. “Explaining Clinical Decision Support Systems in Medical Imaging Using Cycle-Consistent Activation Maximization.” Neurocomputing 458 (October 2021): 141–156. doi:10.1016/j.neucom.2021.05.081.

Li, Haoran, Fazhi He, and Yilin Chen. “Learning Dynamic Simultaneous Clustering and Classification via Automatic Differential Evolution and Firework Algorithm.” Applied Soft Computing 96 (November 2020): 106593. doi:10.1016/j.asoc.2020.106593.

Yang, Chao-Lung, and Nguyen Thi Phuong Quyen. “Data Analysis Framework of Sequential Clustering and Classification Using Non-Dominated Sorting Genetic Algorithm.” Applied Soft Computing 69 (August 2018): 704–718. doi:10.1016/j.asoc.2017.12.019.

Wang, Yulong, Yuan Yan Tang, Cuiming Zou, Luoqing Li, and Hong Chen. “Modal Regression Based Greedy Algorithm for Robust Sparse Signal Recovery, Clustering and Classification.” Neurocomputing 372 (January 2020): 73–83. doi:10.1016/j.neucom.2019.09.056.

Xu, Kaijie, Witold Pedrycz, Zhiwu Li, and Weike Nie. “Optimizing the Prototypes with a Novel Data Weighting Algorithm for Enhancing the Classification Performance of Fuzzy Clustering.” Fuzzy Sets and Systems 413 (June 2021): 29–41. doi:10.1016/j.fss.2020.05.009.

Prajapati, Purvi, and Amit Thakkar. “Performance Improvement of Extreme Multi-Label Classification Using K-Way Tree Construction with Parallel Clustering Algorithm.” Journal of King Saud University - Computer and Information Sciences (March 2021). doi:10.1016/j.jksuci.2021.02.014.

Mouton, Jacques P., Melvin Ferreira, and Albertus S.J. Helberg. “A Comparison of Clustering Algorithms for Automatic Modulation Classification.” Expert Systems with Applications 151 (August 2020): 113317. doi:10.1016/j.eswa.2020.113317.

Luo, Kangqi, Jinyi Lu, Kenny Q. Zhu, Weiguo Gao, Jia Wei, and Meizhuo Zhang. “Layout-Aware Information Extraction from Semi-Structured Medical Images.” Computers in Biology and Medicine 107 (April 2019): 235–247. doi:10.1016/j.compbiomed.2019.02.016.

Tekli, Gilbert. “A Survey on Semi-Structured Web Data Manipulations by Non-Expert Users.” Computer Science Review 40 (May 2021): 100367. doi:10.1016/j.cosrev.2021.100367.

Hasan, Abul, Mark Levene, and David Weston. “Learning Structured Medical Information from Social Media.” Journal of Biomedical Informatics 110 (October 2020): 103568. doi:10.1016/j.jbi.2020.103568.

Tashkandi, Araek, Ingmar Wiese, and Lena Wiese. “Efficient In-Database Patient Similarity Analysis for Personalized Medical Decision Support Systems.” Big Data Research 13 (September 2018): 52–64. doi:10.1016/j.bdr.2018.05.001.

Shaikh, Faiq, Jamshid Dehmeshki, Sotirios Bisdas, Diana Roettger-Dupont, Olga Kubassova, Mehwish Aziz, and Omer Awan. “Artificial Intelligence-Based Clinical Decision Support Systems Using Advanced Medical Imaging and Radiomics.” Current Problems in Diagnostic Radiology 50, no. 2 (March 2021): 262–267. doi:10.1067/j.cpradiol.2020.05.006.

Galvani, Marta, Agostino Torti, Alessandra Menafoglio, and Simone Vantini. “FunCC: A New Bi-Clustering Algorithm for Functional Data with Misalignment.” Computational Statistics & Data Analysis 160 (August 2021): 107219. doi:10.1016/j.csda.2021.107219.

Nooraeni, Rani, Muhamad Iqbal Arsa, and Nucke Widowati Kusumo Projo. “Fuzzy Centroid and Genetic Algorithms: Solutions for Numeric and Categorical Mixed Data Clustering.” Procedia Computer Science 179 (2021): 677–684. doi:10.1016/j.procs.2021.01.055.

Dong, Yihong, Yueting Zhuang, Ken Chen, and Xiaoying Tai. “A Hierarchical Clustering Algorithm Based on Fuzzy Graph Connectedness.” Fuzzy Sets and Systems 157, no. 13 (July 2006): 1760–1774. doi:10.1016/j.fss.2006.01.001.

Piegat, Andrzej. “Fuzzy Control.” Studies in Fuzziness and Soft Computing (2001): 495–607. doi:10.1007/978-3-7908-1824-6_7.

Thong, Nguyen Tho, and Le Hoang Son. “HIFCF: An Effective Hybrid Model between Picture Fuzzy Clustering and Intuitionistic Fuzzy Recommender Systems for Medical Diagnosis.” Expert Systems with Applications 42, no. 7 (May 2015): 3682–3701. doi:10.1016/j.eswa.2014.12.042.

Masulli, Francesco, and Andrea Schenone. “A Fuzzy Clustering Based Segmentation System as Support to Diagnosis in Medical Imaging.” Artificial Intelligence in Medicine 16, no. 2 (June 1999): 129–147. doi:10.1016/s0933-3657(98)00069-4.

Poczeta, Katarzyna, Łukasz Kubuś, and Alexander Yastrebov. “Multidimensional Medical Data Modeling Based on Fuzzy Cognitive Maps and k-Means Clustering.” Procedia Computer Science 176 (2020): 118–127. doi:10.1016/j.procs.2020.08.013.

Wang, Xizhao, Bin Chen, Guoliang Qian, and Feng Ye. “On the Optimization of Fuzzy Decision Trees.” Fuzzy Sets and Systems 112, no. 1 (May 2000): 117–125. doi:10.1016/s0165-0114(97)00386-2.

Crockett, Keeley, Zuhair Bandar, and David Mclean. “On the Optimization of T-Norm Parameters within Fuzzy Decision Trees.” 2007 IEEE International Fuzzy Systems Conference (June 2007). doi:10.1109/fuzzy.2007.4295348.

Haas, Peter J. “Colored Stochastic Petri Nets.” Stochastic Petri Nets (2002): 385–445. doi:10.1007/0-387-21552-2_9.

Budayan, Cenk, Irem Dikmen, and M. Talat Birgonul. “Comparing the Performance of Traditional Cluster Analysis, Self-Organizing Maps and Fuzzy C-Means Method for Strategic Grouping.” Expert Systems with Applications 36, no. 9 (November 2009): 11772–11781. doi:10.1016/j.eswa.2009.04.022.

Askari, Salar. “Fuzzy C-Means Clustering Algorithm for Data with Unequal Cluster Sizes and Contaminated with Noise and Outliers: Review and Development.” Expert Systems with Applications 165 (March 2021): 113856. doi:10.1016/j.eswa.2020.113856.

Ćirić, Miroslav, Aleksandar Stamenković, Jelena Ignjatović, and Tatjana Petković. “Fuzzy Relation Equations and Reduction of Fuzzy Automata.” Journal of Computer and System Sciences 76, no. 7 (November 2010): 609–633. doi:10.1016/j.jcss.2009.10.015.

Dumka, Ankur. “Smart Information Technology for Universal Healthcare.” Healthcare Data Analytics and Management (2019): 211–226. doi:10.1016/b978-0-12-815368-0.00008-7.

Iyawa, Gloria Ejehiohen, Collins Oduor Ondiek, and Jude Odiakaosa Osakwe. “mHealth.” Smart Medical Data Sensing and IoT Systems Design in Healthcare (2020): 1–21. doi:10.4018/978-1-7998-0261-7.ch001.

N., Ambika. “Methodical IoT-Based Information System in Healthcare.” Smart Medical Data Sensing and IoT Systems Design in Healthcare (2020): 155–177. doi:10.4018/978-1-7998-0261-7.ch007.

Badaev, F.I., and T.V. Filippovskaya. “Health Digitalization Alternative: Is There One or Not?” Proceedings of the International Scientific and Practical Conference on Digital Economy (ISCDE 2019) (7-8 November 2019): 150-153. doi:10.2991/iscde-19.2019.28.


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DOI: 10.28991/esj-2021-01305

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Copyright (c) 2021 Abas Hasanovich Lampezhev, Elena Yur`evna Linskaya, Aslan Adal`bievich Tatarkanov, Islam Alexandrovich Alexandrov