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A Novel Structure of Highly Interpretable Fuzzy Rules Extraction

Fatemeh Ahouz, Amin Golabpour



Introduction: Extracting effective rules from medical data with two indicators of accuracy and high interpretability is essential to increase the accuracy and speed of diagnosis by specialists. As a result, the production of medical assistant systems that are able to detect the rules governing the data plays a vital role in early detection of the disease and thus increase the chances of treatment, disease control and maintaining the quality of life of patients.

Material and Methods: In this paper, a system of automatic extraction of rules from medical data by a new hybrid method based on fuzzy logic and genetic algorithm is presented. Genetic algorithms are used to automatically generate these rules. The Parkinson UCI dataset including 195 records and 23 variables was used to evaluate the proposed method based on the criteria of interpretability, accuracy, sensitivity and specificity.

Results: The evaluation of the proposed model on the Parkinson's dataset was the accuracy of 84.62%. This accuracy is supported by 4 fuzzy rules with an average rule length of 2 and using 7 linguistic terms extremely low, very low, low, normal, high, very high and extremely high. All fuzzy membership functions that represent each term have the same width.

Conclusion: The proposed method, based on the three criteria of low number of rules, short rule length and symmetric membership functions with equal width for all variables, is quite suitable for automatic production of accurate and compact rules with high interpretability in medical data. . A 90% dimensionality reduction in the experimental evaluation showed that this model could be used to implement real-time systems.


Feng TC, Li THS, Kuo PH. Variable coded hierarchical fuzzy classification model using DNA coding and evolutionary programming. Applied Mathematical Modelling. 2015; 39(23-24): 7401-19.

De Santis E, Rizzi A, Sadeghian AJASC. Hierarchical genetic optimization of a fuzzy logic system for energy flows management in microgrids. Applied Soft Computing. 2017; 60: 135-49.

Tan CH, Tan MS, Chang SW, Yap KS, Yap HJ, Wong SY. Genetic algorithm fuzzy logic for medical knowledge-based pattern classification. Journal of Engineering Science and Technology. 2018; 13(Special Issue on ICCSIT 2018): 242-58.

Ishibuchi H, Nojima Y, Kuwajima I. Genetic rule selection as a postprocessing procedure in fuzzy data mining. International Symposium on Evolving Fuzzy Systems. IEEE; 2006.

Gorzalczany MB, Rudzinski F. Interpretable and accurate medical data classification: A multi-objective genetic-fuzzy optimization approach. Expert Systems with Applications. 2017; 71: 26-39.

Mitra S, Hayashi Y. Neuro-fuzzy rule generation: Survey in soft computing framework. IEEE Transactions on Neural Networks. 2000; 11(3): 748-68.

Shi Y, Eberhart R, Chen Y. Implementation of evolutionary fuzzy systems. IEEE Transactions on Fuzzy Systems. 1999; 7(2): 109-19.

Chang X, Lilly JH. Evolutionary design of a fuzzy classifier from data. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics). 2004; 34(4): 1894-906.

GaneshKumar P, Rani C, Devaraj D, Victoire TAA. Hybrid Ant Bee Algorithm for Fuzzy Expert System Based Sample Classification. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2014; 11(2): 347-60.

Shortliffe E, Cimino JJ. Biomedical informatics: Computer applications in health care and biomedicine. 4th ed. Springer; 2013.

Sujatha R, Ephzibah EP, Dharinya S, Uma Maheswari G, Mareeswari V, Pamidimarri V. Comparative study on dimensionality reduction for disease diagnosis using fuzzy classifier. International Journal of Engineering and Technology. 2018; 7(1): 79-84.

Seera M, Lim CP. A hybrid intelligent system for medical data classification. Expert Systems with Applications. 2014; 41(5): 2239-49.

UCI Machine Learning Repository. Parkinsons data set [Internet]. 2007 [cited: 15 Jun 2020]. Available from: http://archive.ics.uci.edu/ml/datasets/Parkinsons

Cai Z, Gu J, Wen C, Zhao D, Huang C, Huang H, et al. An intelligent Parkinson's disease diagnostic system based on a chaotic bacterial foraging optimization enhanced fuzzy KNN approach. Comput Math Methods Med. 2018; 2018: 2396952. PMID: 30034509 DOI: 10.1155/2018/2396952

Li Y, Swift S, Tucker A. Modelling and analysing the dynamics of disease progression from cross-sectional studies. J Biomed Inform. 2013; 46(2): 266-74. PMID: 23200810 DOI: 10.1016/j.jbi.2012.11.003

Pahuja G, Nagabhushan TN. A Comparative Study of Existing Machine Learning Approaches for Parkinson's Disease Detection. IETE Journal of Research. 2018; 2018: 1-11.

Devarajan M, Ravi L. Intelligent cyber-physical system for an efficient detection of Parkinson disease using fog computing. Multimedia Tools and Applications. 2019; 78: 32695–719.

Olivares R, Munoz R, Soto R, Crawford B, Cárdenas D, Ponce A, et al. An optimized brain-based algorithm for classifying parkinson's disease. Appllied Science. 2020; 10(5). 1827.

Parkinson's Foundation. Statistics [Internet]. 2020 [cited: 1 Jun 2020]. Available from: https://www.parkinson.org/Understanding-Parkinsons/Statistics

Hariharan M, Polat K, Sindhu R. A new hybrid intelligent system for accurate detection of Parkinson's disease. Comput Methods Programs Biomed. 2014; 113(3): 904-13. PMID: 24485390 DOI: 10.1016/j.cmpb.2014.01.004

Marar S, Swain D, Hiwarkar V, Motwani N, Awari A. Predicting the occurrence of Parkinson's Disease using various classification models. International Conference on Advanced Computation and Telecommunication. IEEE; 2018.

Caesarendra W, Putri FT, Ariyanto M, Setiawan JD. Pattern recognition methods for multi stage classification of Parkinson's disease utilizing voice features. IEEE International Conference on Advanced Intelligent Mechatronics. IEEE; 2015.

Avci D, Dogantekin A. An expert diagnosis system for Parkinson disease based on genetic algorithm, wavelet kernel, extreme learning machine. Parkinsons Dis. 2016; 2016: 5264743. PMID: 27274882 DOI: 10.1155/2016/5264743

Abiyev RH, Abizade S. Diagnosing Parkinson's diseases using fuzzy neural system. Comput Math Methods Med. 2016; 2016: 1267919. PMID: 26881009 DOI: 10.1155/2016/1267919

Dash S, Thulasiram R, Thulasiraman P. An enhanced chaos-based firefly model for Parkinson's disease diagnosis and classification. International Conference on Information Technology (ICIT). IEEE; 2018.

Tomar D, Prasad BR, Agarwal S. An efficient Parkinson disease diagnosis system based on least squares twin support vector machine and particle swarm optimization. International Conference on Industrial and Information Systems (ICIIS). IEEE; 2015.

Karunanithi D, Rodrigues P. A fuzzy rule-based diagnosis of Parkinson’s disease. International Conference on ISMAC in Computational Vision and Bio-Engineering. Springer; 2019.

Lee SH. Feature selection based on the center of gravity of BSWFMs using NEWFM. Engineering Applications of Artificial Intelligence. 2015; 45: 482-7.

Hsieh Y-Z, Su M-C, Wang P-C. A PSO-based rule extractor for medical diagnosis. J Biomed Inform. 2014; 49: 53-60. PMID: 24835617 DOI: 10.1016/j.jbi.2014.05.001

Little M, McSharry P, Roberts S, Costello D, Moroz I. Exploiting nonlinear recurrence and fractal scaling properties for voice disorder detection. Biomed Eng Online. 2007; 6: 23. PMID: 17594480 DOI: 10.1186/1475-925X-6-23

Ahouz F, Golabpour A. A novel diagnostic rule for Parkinson's disease based on a hybrid extraction method. Journal of Knowledge & Health in Basic Medical Sciences. 2020; 15(2): 42-50.

Setnes M, Roubos H. GA-fuzzy modeling and classification: Complexity and performance. IEEE Transactions on Fuzzy Systems, 2000. 8(5): 509-22.

Mansourypoor F, Asadi S. Development of a reinforcement learning-based evolutionary fuzzy rule-based system for diabetes diagnosis. Comput Biol Med. 2017; 91: 337-52. PMID: 29121541 DOI: 10.1016/j.compbiomed.2017.10.024

Wang YY, Li J. Feature-selection ability of the decision-tree algorithm and the impact of feature-selection/extraction on decision-tree results based on hyperspectral data. International Journal of Remote Sensing. 2008; 29(10): 2993-3010.

DOI: http://dx.doi.org/10.30699/fhi.v10i1.253


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