Abstract
Expert’s knowledge base systems are not effective as a decision-making aid for physicians in providing accurate diagnosis and treatment of heart diseases due to vagueness in information and impreciseness and uncertainty in decision making. For this reason, automatic diagnostic fuzzy systems are very time demanding to improve the diagnostic accuracy. In this paper, we have developed an automatic fuzzy diagnostic system based on genetic algorithm (GA) and a modified dynamic multi-swarm particle swarm optimization (MDMS-PSO) for prognosticating the risk level of heart disease. Our proposed fuzzy diagnostic system (FS) works as follows: i) Preprocess the data sets ii) Effective attributes are selected through statistical methods such as Correlation coefficient, R-Squared and Weighted Least Squared (WLS) method, iii) Weighted fuzzy rules are formed on the basis of selected attributes using GA, iv) MDMS-PSO is employed for the optimization of membership functions (MFs) of FS, v) Build the ensemble FS from the generated fuzzy knowledge base by fusing the different local FSs. Finally, to ascertain the efficiency of the adaptive FS, the applicability of the FS is appraised with quantitative, qualitative and comparative analysis on the publicly available different real-life data sets. From the empirical analysis, we see that this hybrid model can manage the knowledge vagueness and decision-making uncertainty precisely and it has yielded better accuracy on the different publicly available heart disease data sets than other existing methods so that it justifies its adaptability with different data sets.
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References
Alayón S, Robertson R, Warfield SK, Ruiz-Alzola J (2007) A fuzzy system for helping medical diagnosis of malformations of cortical development. J Biomed Inform 40(3):221–235. https://doi.org/10.1016/j.jbi.2006.11.002
Chen X, Wang Y, Zhen S, Huang K, Zhao H, Chen YH (2016) Robust control design of uncertain mechanical systems: a fuzzy approach. Intern J Uncert Fuz Knowl-Based Syst 24(2):307–324. https://doi.org/10.1142/S021848851650015X
Saeed S, Niknafs A (2016) Artificial bee colony-fuzzy q learning for reinforcement fuzzy control (truck backer-upper control problem). Intern J Uncert Fuz Knowl-Based Syst 24(1):123–136. https://doi.org/10.1142/S0218488516500070
Lee CC (1990) Fuzzy logic in control systems: fuzzy logic controller - Part 1. https://doi.org/10.1109/21.52552
Stavrakoudis DG, Theocharis JB (2012) Handling highly-dimensional classification tasks with hierarchical genetic fuzzy rule-based classifiers. Intern J Uncert Fuz Knowl-Based Syst 20(supp02):73–104. https://doi.org/10.1142/S0218488512400168
Bahrami B, Shafiee M (2015) Fuzzy descriptor models for earthquake time prediction using seismic time series. Intern J Uncert Fuz Knowl-Based Syst 23(4):505–519. https://doi.org/10.1142/S0218488515500221
Tay K, Lim C (2011) On monotonic sufficient conditions of fuzzy inference systems and their applications. Intern J Uncert Fuz Knowl-Based Syst 19(5):731–757. https://doi.org/10.1142/S0218488511007210
Kubota N, Yaguchi A (2011) Decision making of robot partners based on fuzzy control and boltzmann selection. Intern J Uncert Fuz Knowl-Based Syst 19(3):529–545. https://doi.org/10.1142/S0218488511007118
Zulueta Y, MartÍNez-Moreno J, PÉRez RB, MartÍNez L (2014) A discrete time variable index for supporting dynamic multi-criteria decision making. Intern J Uncert Fuz Knowl-Based Syst 22(1):1–22. https://doi.org/10.1142/S0218488514500019
Bustince H, Barrenechea E, Pagola M (2007) Image thresholding using restricted equivalence functions and maximizing the measures of similarity. Fuzzy Sets Syst 158(5):496–516. https://doi.org/10.1016/j.fss.2006.09.012
Khatibi V, Montazer GA (2009) Intuitionistic fuzzy set vs. fuzzy set application in medical pattern recognition. Artif Intell Med 47(1):43–52. https://doi.org/10.1016/j.artmed.2009.03.002
Adeli A, Neshat M (2010) A fuzzy expert system for heart disease diagnosis. In: Proceedings of the international multi-conference of engineers and computer scientists, vol I, pp 1–6
Ephzibah EP, Sundarapandian V (2012) A neuro fuzzy expert system for heart disease diagnosis. Computer Science & Engineering: An International Journal (CSEIJ) 2(1):17
Saravanakumar S, Rinesh S (2014) Effective heart disease prediction using frequent feature selection method. J Innov Res Comput Commun Eng 2(1):2767–2774
Wisaeng K (2014) Predict the diagnosis of heart disease using feature selection and k-nearest neighbor algorithm. Appl Math Sci 8(83):4103–4113. https://doi.org/10.12988/ams.2014.45382
Xu Y, Pan X, Zhou Z, Yang Z, Zhang Y (2015) Structural least square twin support vector machine for classification. Appl Intell 42(3):527–536. https://doi.org/10.1007/s10489-014-0611-4
Delgado M (2003) Mining fuzzy association rules : an overview. In: BISC conference, december, pp 397–410 https://doi.org/10.1145/266714.266898
Duch W, Adamczak R, Grabczewski K (2001) A new methodology of extraction, optimization and application of crisp and fuzzy logical rules. IEEE Trans Neural Netw 12(2):277–306. https://doi.org/10.1109/72.914524
Straszecka E (2006) Combining uncertainty and imprecision in models of medical diagnosis. Inf Sci 176 (20):3026–3059. https://doi.org/10.1016/j.ins.2005.12.006
Detrano R, Janosi A, Steinbrunn W, Pfisterer M, Schmid JJ, Sandhu S, Guppy KH, Lee S, Froelicher V (1989) International application of a new probability algorithm for the diagnosis of coronary artery disease. Am J Cardio 64(5):304–310. https://doi.org/10.1016/0002-9149(89)90524-9
Cheung N (2001) Machine learning techniques for medical analysis. School of Information Technology and Electrical Engineering, BSc Thesis, University of Queenland
Das R, Turkoglu I, Sengur A (2009) Effective diagnosis of heart disease through neural networks ensembles. Expert Syst Appl 36(4):7675–7680. https://doi.org/10.1016/j.eswa.2008.09.013
Polat K, Sahan S, Günes S (2006) A new method to medical diagnosis: artificial immune recognition system (AIRS) with fuzzy weighted pre-processing and application to ECG arrhythmia. Expert Syst Appl 31(2):264–269. https://doi.org/10.1016/j.eswa.2005.09.019
Polat K, Sahan S, Günes S (2007) Automatic detection of heart disease using an artificial immune recognition system (AIRS) with fuzzy resource allocation mechanism and k-nn (nearest neighbour) based weighting preprocessing. Expert Syst Appl 32(2):625–631. https://doi.org/10.1016/j.eswa.2006.01.027
El-Bialy R, Salamay MA, Karam OH, Khalifa ME (2015) Feature analysis of coronary artery heart disease data sets. Procedia Comput Sci 65:459–468. https://doi.org/10.1016/j.procs.2015.09.132
Rout S (2012) Fuzzy petri net application: heart disease diagnosis
Khatibi V, Montazer GA (2010) A fuzzy-evidential hybrid inference engine for coronary heart disease risk assessment. Expert Syst Appl 37(12):8536–8542. https://doi.org/10.1016/j.eswa.2010.05.022
Tsipouras MG, Exarchos TP, Fotiadis DI, Kotsia AP, Vakalis KV, Naka KK, Michalis LK (2008) Automated diagnosis of coronary artery disease based on data mining and fuzzy modeling. IEEE Transactions on Information Technology in Biomedicine : A Publication of the IEEE Engineering in Medicine and Biology Society 12(4):447–458. https://doi.org/10.1109/TITB.2007.907985
Anooj P (2012) Clinical decision support system: risk level prediction of heart disease using weighted fuzzy rules. Journal of King Saud University - Computer and Information Sciences 24 (1):27–40. https://doi.org/10.1016/j.jksuci.2011.09.002
Kahramanli H, Allahverdi N (2008) Design of a hybrid system for the diabetes and heart diseases. Expert Syst Appl 35(1-2):82–89. https://doi.org/10.1016/j.eswa.2007.06.004
Kusy M, Zajdel R (2014) Probabilistic neural network training procedure based on q(0)-learning algorithm in medical data classification. Appl Intell 41(3):837–854. https://doi.org/10.1007/s10489-014-0562-9
Ephzibah EP (2011) A hybrid genetic-fuzzy expert system for effective heart disease diagnosis. In: Communications in computer and information science, vol 198 CCIS. pp 115–121, https://doi.org/10.1007/978-3-642-22555-0_13
tuz jakirin S, Ferdaus AA, Khan MA (2014) A genetic algorithm approach using improved fitness function for classification rule mining. Int J Comput Appl 97(23):12–18
Mankad K, Sajja PS, Akerkar R (2011) Evolving rules using genetic fuzzy approach - an educational case study. International Journal on Soft Computing (IJSC) 2(1):35–46
Alcala R, Alcala-Fdez J, Herrera F (2007) A proposal for the genetic lateral tuning of linguistic fuzzy systems and its interaction with rule selection. IEEE Trans Fuzzy Syst 15(4):616–635. https://doi.org/10.1109/TFUZZ.2006.889880
Chandra Debnath SB, Chandra Shill P, Murase K (2013) Particle swarm optimization based adaptive strategy for tuning of fuzzy logic controller. Int J Artif Intell Appl 4(1):37–50. https://doi.org/10.5121/ijaia.2013.4104
Vaneshani S, Jazayeri-rad H (2011) Optimized fuzzy control by particle swarm optimization technique for control of CSTR. Intern J Comput Elect Autom Control Inform Eng 5(11):686–691
Das D, Ghosh A (2013) Algorithm for a PSO tuned fuzzy controller of a DC motor. Int J Comput Appl 73(4):37–41
Permana KE, Hashim SZM (2010) Fuzzy membership function generation using particle swarm optimization. International Journal of Open Problems in Computer Science and Mathematics IJOPCM 3(1):27–41
Fang G, Kwok NM, Ha Q (2008) Automatic fuzzy membership function tuning using the particle swarm optimization. In: 2008 IEEE pacific-asia workshop on computational intelligence and industrial application, vol 2. IEEE, pp 324–328 https://doi.org/10.1109/PACIIA.2008.105
Bastian A (1994) How to handle the flexibility of linguistic variables with applications. Intern J Uncert Fuz Knowl-Based Syst 2(4):463–484. https://doi.org/10.1142/S0218488594000365
(1988-07-01(Accessed december 7, 2015)) UCI machine learning repository: heart disease data set. http://archive.ics.uci.edu/ml/datasets/Heart+Disease
(Accessed december 7, 2015) uci machine learning repository: Statlog (heart) data set. http://archive.ics.uci.edu/ml/datasets/Statlog+(Heart)
Han J, Kamber M, Pei J (2012) Data mining concepts and techniques, 3rd edn
Jilani T, Yasin H, Yasin M, Ardil C (2009) Acute coronary syndrome prediction using data mining techniques-an application. In: World academy of science, pp 474–478
Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157–1182
(Accessed december 7, 2015) Coefficient of determination - Wikipedia, the free encyclopedia. https://en.wikipedia.org/wiki/Coefficient_of_determination
(Accessed december 7, 2015) Least squares - Wikipedia, the free encyclopedia. https://en.wikipedia.org/wiki/Least_squares#Weighted_least_squares
Michalewicz Z (1996) Genetic algorithms + data structures = evolution programs (3rd ed.), vol 1. https://doi.org/10.2307/2669583
Liang JJ, Suganthan PN (2005) Dynamic multi-swarm particle swarm optimizer. In: Proceedings - 2005 IEEE swarm intelligence symposium, SIS 2005, pp 127–132 https://doi.org/10.1109/SIS.2005.1501611
Mohebbi H, Mu Y, Ding W (2017) Learning weighted distance metric from group level information and its parallel implementation. Appl Intell 46(1):180–196. https://doi.org/10.1007/s10489-016-0826-7
Polat K, Günes S (2009) A new feature selection method on classification of medical datasets: Kernel f-score feature selection. Expert Syst Appl 36(7):10,367–10,373. https://doi.org/10.1016/j.eswa.2009.01.041
Fernández-Delgado M, Cernadas E, Barro S, Amorim D (2014) Do we need hundreds of classifiers to solve real world classification problems? J Mach Learn Res 15:3133–3181
Polat K, Günes S, Tosun S (2006) Diagnosis of heart disease using artificial immune recognition system and fuzzy weighted pre-processing. Pattern Recogn 39(11):2186–2193. https://doi.org/10.1016/j.patcog.2006.05.028
Kukar M, Kononenko I, Grošelj C, Kralj K, Fettich J (1999) Analysing and improving the diagnosis of ischaemic heart disease with machine learning. Artif Intell Med 16(1):25–50. https://doi.org/10.1016/S0933-3657(98)00063-3
Akay M (1992) Noninvasive diagnosis of coronary artery disease using a neural network algorithm. Biol Cybern 67(4):361–7
Gennari JH, Langley P, Fisher D (1989) Models of incremental concept formation. Artif Intell 40(1-3):11–61. https://doi.org/10.1016/0004-3702(89)90046-5
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Paul, A., Shill, P., Rabin, M. et al. Adaptive weighted fuzzy rule-based system for the risk level assessment of heart disease. Appl Intell 48, 1739–1756 (2018). https://doi.org/10.1007/s10489-017-1037-6
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DOI: https://doi.org/10.1007/s10489-017-1037-6