FFBAT-Optimized Rule Based Fuzzy Logic Classifier for Diabetes

Abstract:

In the last two decades, developing countries are facing heavy increase in diabetes among their population that is leading to other severe diseases. Hence, there is a great need to develop some effective prediction methods to prevent diabetes. In this paper an attempt has been made to develop Firefly-BAT (FFBAT) optimized Rule Based Fuzzy Logic (RBFL) prediction algorithm for diabetes. The algorithm has two main steps. First, Locality Preserving Projections (LPP) algorithm is used for feature reduction and then classification of diabetes is done by means of RBFL classifier. LPP algorithm has been used to identify the related attributes and then the fuzzy rules are produced from RBFL. The rules are optimized using FFBAT algorithm. Next, the fuzzy system is designed with the help of optimized fuzzy rules and membership functions that will classify the diabetes data. FFBAT is the optimization algorithm which combines the features of BAT and Firefly (FF) optimization techniques. The experiment analysis shows that the RBFL-FFBAT algorithm outperforms the existing approaches.

• Periodical: International Journal of Engineering Research in Africa (Volume 24)
• Pages: 137-152
• DOI: https://doi.org/10.4028/www.scientific.net/JERA.24.137
• Online since: June 2016
• Authors: G. Thippa Reddy*, Neelu Khare
• Keywords: Bat Algorithm, Firefly Algorithm, Optimization, Soft Computing

References

[1] Aiswarya Iyer, S. Jeyalatha and Ronak Sumbaly, DIAGNOSIS OF DIABETES USING CLASSIFICATION MINING TECHNIQUES, International Journal of Data Mining & Knowledge Management Process (IJDKP) Vol. 5, No. 1, January (2015).

DOI: 10.5121/ijdkp.2015.5101

[2] Xin-She Yang, Bat Algorithm: Literature Review and Applications, International Journal of Bio-Inspired Computation, Vol. 5, No. 3, p.141–149 (2013).

[3] Sashikala Mishra, Kailash Shaw, Debahuti Mishra, A New Meta-heuristic Bat Inspired Classification Approach for Microarray Data, SciVerse ScienceDirect, Procedia Tecgnology 4 (2012) pp.802-806.

DOI: 10.1016/j.protcy.2012.05.131

[4] AhmedMajid Taha, Aida Mustapha, and Soong-Der Chen, Naive Bayes-Guided Bat Algorithm for Feature Selection, Hindawi Publishing Corporation, The ScientificWorld Journal. Volume (2013).

DOI: 10.1155/2013/325973

[5] Xin-She Yang. Amir Hossein Gandomi, Bat algorithm: a novel approach for global engineering optimization, Engineering Computations: International Journal for Computer-Aided Engineering and Software Vol. 29 No. 5, (2012).

DOI: 10.1108/02644401211235834

[6] Goutam Das, Bat algorithm based Softcomputing Approach to Perceive Hairline Bone Fracture in Medical X-ray Images, International Journal of Computer Science & Engineering Technology (IJCSET), Vol. 4 No. 04 Apr (2013).

[7] Iztok Fister, Iztok Fister Jr, Xin-She Yang, Janez Brest, A comprehensive review of firefly algorithms, Swarm and Evolutionary Computation, Volume 13, December 2013, Pages 34–46.

DOI: 10.1016/j.swevo.2013.06.001

[8] Tahereh Hassanzadeh, Mohammad Reza Meybodi, A New Hybrid Approach for Data Clustering using Firefly Algorithm and K-means, The 16th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP 2012).

DOI: 10.1109/aisp.2012.6313708

[9] V.P. Archana Linnet Hailey, N. Sudha, An Optimization Approach of Firefly Algorithm to Record Deduplication, International Journal of Engineering Research & Technology (IJERT), Vol. 2 Issue 9, September – (2013).

[10] Ehsan Sadeghipour, Nasrollah Sahragard, Mahmood-Reza Sayebani and Rahman mahdizadeh, BREAST CANCER DETECTION BASED ON A HYBRID APPROACH OF FIREFLY ALGORITHM AND INTELLIGENT SYSTEMS, Indian Journal of Fundamental and Applied Life Sciences, 2015 Vol. 5 (S1), pp.468-472.

[11] Sarac E.  , Ozel S.A. , Web page classification using firefly optimization, Innovations in Intelligent Systems and Applications (INISTA), 2013 IEEE International Symposium.

DOI: 10.1109/inista.2013.6577619

[12] Xin-She Yang and Xingshi He, (2013). Firefly Algorithm: Recent Advances and Applications, Int. J. Swarm Intelligence, Vol. 1, No. 1, p.36–50. DOI: 10. 1504/IJSI. 2013. 055801.

DOI: 10.1504/ijsi.2013.055801

[13] Xin-She Yang , Firefly Algorithm, Levy Flights and Global Optimization, Research and Development in Intelligent Systems XXVI, DOI 10. 1007/978-1-84882-983-1_15, © Springer-Verlag London Limited (2010).

[14] Saibal K. Pal , C. S Rai , Amrit Pal Singh , Comparative Study of Firefly Algorithm and Particle Swarm Optimization for Noisy NonLinear Optimization Problems, I.J. Intelligent Systems and Applications, 2012, 10, 50-57.

DOI: 10.5815/ijisa.2012.10.06

[15] Theofanis Apostolopoulos and Aristidis Vlachos, Application of the Firefly Algorithm for Solving the Economic Emissions Load Dispatch Problem, Hindawi Publishing Corporation International Journal of Combinatorics Volume (2011).

DOI: 10.1155/2011/523806

[16] Hema Banati and Monika Bajaj, Fire Fly Based Feature Selection Approach, IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 4, No 2, July (2011).

[17] A. R. Webb, Statistical Pattern Recognition. John Wiley & Sons, (2003).

[18] A. K. Jain et al., Statistical pattern recognition: a review, IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 1, pp.4-37, Jan (2000).

[19] N. Kwak and C. H. Choi, Input feature selection by mutual information based on Parzen window, IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 12, pp.1667-1671, Dec (2002).

DOI: 10.1109/tpami.2002.1114861

[20] P. Langley, Selection of relevant features in machine learning. Defense Technical Information Center, (1994).

[21] R. Kohavi, and G. John, Wrapper for feature subset selection, Artificial Intell., vol. 97, no. 1, pp.273-324, (1997).

DOI: 10.1016/s0004-3702(97)00043-x

[22] Kalyani Mali and Samayita Bhattacharya, Soft Computing on Medical-Data (SCOM) for a Countrywide Medical System using Data Mining and Cloud Computing Features, Global Journal of Computer Science and Technology Cloud and Distributed, vol. 13, no. 3, (2013).

DOI: 10.26634/jse.7.1.1962

[23] Divya and S. Agarwal, Weighted support vector regression approach for remote healthcare monitoring, in Proc. IEEE Int. Conf. on Recent Trends in Inform. Technology, 2011, pp.969-974.

DOI: 10.1109/icrtit.2011.5972437

[24] Wolberg W-H, Street W-N, Mangasarian, Machine learning techniques to diagnose breast cancer from fine-needle aspirates, Cancer Lett., vol. 77, (1994).

DOI: 10.1016/0304-3835(94)90099-x

[25] Kurgan, Cios, Tadeusiewicz, Ogiela, Goodenday, Knowledge discovery approach to automated cardiac SPECT diagnosis, Artificial Intelligence in Medicine, vol. 23pp. 49–69, (2001).

DOI: 10.1016/s0933-3657(01)00082-3

[26] Antoniadis A, Lambert-Lacroix S, Leblanc F. Effective dimension reduction methods for tumor classification using gene expression data, Bioinformatics, vol. 19, no. 5, 563–70, (2003).

DOI: 10.1093/bioinformatics/btg062

[27] I. Guyon, J. Weston, S. Barnhill, V. Vapnik, "Gene selection for cancer classification using support vector machines. Machine Learning, vol. 46, p.389–422, (2002).

DOI: 10.1023/a:1012487302797

[28] Yu J-S, Ongarello S, Fiedler R, Chen X-W, Toffolo G, Cobelli C, Ovarian cancer identification based on dimensionality reduction for high-throughput mass spectrometry data, Bioinformatics, (2005).

DOI: 10.1093/bioinformatics/bti370

[29] P. Cunningham, J.G. Carney, Diversity versus quality in classification ensembles based on feature selection, 11th European Conference on Machine Learning (2000).

DOI: 10.1007/3-540-45164-1_12

[30] Jollife, I. T. Principal Component Analysis, Springer-Verlag, New York. (1986).

[31] M. I. Schmidt et al., Detection of undiagnosed diabetes and other hyperglycemia states: the atherosclerosis risk in communities study, Diabetes Care, vol. 26, no. 5, p.1338– 1343, (2003).

[32] P.W. Wilson et al., Prediction of incident diabetes mellitus in middleaged adults: the Framingham offspring study, Archives of Internal Medicine, vol. 167, no. 10, pp.1068-1074, (2007).

DOI: 10.1001/archinte.167.10.1068

[33] J. Han and M. Kamber, Data Mining: Concepts and Techniques, 3rd ed. Morgan kaufmann, (2011).

[34] D. Delen et al., Predicting breast cancer survivability: a comparison of three data mining methods, Artificial Intell. in Medicine, vol. 34, no. 2, pp.113-127, (2005).

DOI: 10.1016/j.artmed.2004.07.002

[35] N. Rathore et al., Predicting the survivability of breast cancer patients using ensemble approach, in Proc. IEEE Int. Conf. Issues and Challenges in Intelligent Computing Techniques, 2014, pp.459-464.

DOI: 10.1109/icicict.2014.6781326

[36] P. Herron, Machine learning for medical decision support: evaluating diagnostic performance of machine learning classification algorithms, INLS 110, Data Mining, (2004).

[37] J. C. Prather et al., Medical data mining: knowledge discovery in a clinical data warehouse, in Proc. American Medical Informatics Association Annu. fall Symp., 1997, pp.101-105.

[38] S. B. Patil and Y. S. Kumaraswamy, Intelligent and effective heart attack prediction system using data mining and artificial neural network, European J. Scientific Research, vol. 31, no. 4, pp.642-656, (2009).

[39] G. Richards et al., Data mining for indicators of early mortality in a database of clinical records, Artificial Intell. in Medicine, vol. 22, no. 3, pp.215-231, (2001).

DOI: 10.1016/s0933-3657(00)00110-x

[40] S. W. Fei, Diagnostic study on arrhythmia cordis based on particle swarm optimization-based support vector machine, Expert Syst. With Applicat., vol. 37, no. 10, pp.6748-6752, (2010).

DOI: 10.1016/j.eswa.2010.02.126

[41] J. Han and M. Kamber, Data Mining: Concepts and Techniques, 3rd ed. Morgan kaufmann, (2011).

[42] D. Delen et al., Predicting breast cancer survivability: a comparison of three data mining methods, Artificial Intell. in Medicine, vol. 34, no. 2, pp.113-127, (2005).

DOI: 10.1016/j.artmed.2004.07.002

[43] H. Jianchao et al., Diabetes data analysis and prediction model discovery using RapidMiner, in Proc. 2nd IEEE Int. Conf. Future Generation Commun. and Networking, 2008, vol. 3, pp.96-99.

[44] Ning Wang, Guixia Kang, A monitoring system for type 2 diabetes mellitus, IEEE 14th International Conference on e-Health Networking, Applications and Services (Healthcom) (2012).

DOI: 10.1109/healthcom.2012.6380067

[45] N. Rathore et al., Predicting the survivability of breast cancer patients using ensemble approach, in Proc. IEEE Int. Conf. Issues and Challenges in Intelligent Computing Techniques, 2014, pp.459-464.

DOI: 10.1109/icicict.2014.6781326

[46] H. Liu and H. Motoda, Feature Selection for Knowledge Discovery and Data Mining. Springer, Boston: Kluwer Academic Publishers, (1998).

[47] H. Jianchao et al., Diabetes data analysis and prediction model discovery using RapidMiner, in Proc. 2nd IEEE Int. Conf. Future Generation Commun. and Networking, 2008, vol. 3, pp.96-99.

[48] Nahla H. Barakat, Andrew P. Bradley, Senior Member, IEEE, and Mohamed Nabil H. Barakat", Intelligible Support Vector Machines for Diagnosis of Diabetes Mellitus", Information Technology in Biomedicine, IEEE Transactions (2010).

DOI: 10.1109/titb.2009.2039485

[49] Qing Ang, ZhiWen Liu, Weidong Wang, Kaiyuan Li, Explored research on data pre-processing and mining technology for clinical data applications, The 2nd IEEE International Conference on Information Management and Engineering (ICIME), (2010).

DOI: 10.1109/icime.2010.5477660

[50] N. Yuvaraj and P. Vivekanandan, An Efficient SVM based Tumor Classification with Symmetry Non-Negative Matrix Factorization Using Gene Expression Data, Information Communication and Embedded Systems, p.761 – 768, (2013).

DOI: 10.1109/icices.2013.6508193

[51] B. Dennis, S. Muthukrishnan, AGFS: Adaptive Genetic Fuzzy System for medical data classification, Applied Soft Computing, Vol. 25, pp.242-252, (2014).

DOI: 10.1016/j.asoc.2014.09.032

[52] X. S. Yang, A New Metaheuristic Bat-Inspired Algorithm, in: Nature Inspired Cooperative Strategies for Optimization, (NISCO 2010) (Eds. J. R. Gonzalez et al. ), Studies in Computational Intelligence, Springer Berlin, 284, Springer, 65-74 (2010).

DOI: 10.1007/978-3-642-12538-6_6

[53] Yang, X. S. (2009). Firefly algorithms for multimodal optimization, Stochastic Algorithms: Foundations and Applications, SAGA 2009. Lecture Notes in Computer Sciences 5792. p.169–178.

DOI: 10.1007/978-3-642-04944-6_14

[54] H, B. Sandya, P. Hemanth Kumar, Himanshi Bhudiraja and Susham K. Rao, Fuzzy Rule Based Feature Extraction and Classification of Time Series Signal, International Journal of Soft Computing and Engineering (IJSCE), (2013).

[55] X. He and P. Niyogi, Locality preserving projections, In Advances in Neural Information Processing Systems Cambridge, MA: MIT Press, (2003).

[56] K. Srinivas, G. Raghavendra Rao and A. Govardhan, Rough-Fuzzy Classifier: A System to Predict the Heart Disease by Blending Two Different Set Theories, Research article - computer engineering and computer science, (2014).

DOI: 10.1007/s13369-013-0934-1

[57] R.S. Parpinelli and H.S. Lopes, New inspirations in swarm intelligence: a survey, International Journal of Bio-Inspired Computation, vol. 3, p.1–16, (2011).

DOI: 10.1504/ijbic.2011.038700

[58] Jun, L., Liheng, L., & Xianyi, W. (2015). A double-subpopulation variant of the bat algorithm,. Applied Mathematics and Computation, 263, 361-377.

DOI: 10.1016/j.amc.2015.04.034

[59] Meng, X. B., Gao, X. Z., Liu, Y., & Zhang, H. (2015). A novel bat algorithm with habitat selection and Doppler effect in echoes for optimization,. Expert Systems with Applications, 42(17), 6350-6364.

DOI: 10.1016/j.eswa.2015.04.026

[60] Yılmaz, S., & Küçüksille, E. U. (2015). A new modification approach on bat algorithm for solving optimization problems,  Applied Soft Computing, 28, 259-275.

DOI: 10.1016/j.asoc.2014.11.029

[61] Isola, R., Carvalho, R., & Tripathy, A. K. (2012). Knowledge Discovery in Medical Systems Using Differential Diagnosis, LAMSTAR, and-NN, Information Technology in Biomedicine, IEEE Transactions on, 16(6), 1287-1295.

DOI: 10.1109/titb.2012.2215044

[62] Nie, L., Zhao, Y. L., Akbari, M., Shen, J., & Chua, T. S. (2015). Bridging the vocabulary gap between health seekers and healthcare knowledge,. Knowledge and Data Engineering, IEEE Transactions on, 27(2), 396-409.

DOI: 10.1109/tkde.2014.2330813

[63] Nie, L., Wang, M., Zhang, L., Yan, S., Zhang, B., & Chua, T. S. (2015). Disease inference from health-related questions via sparse deep learning,. Knowledge and Data Engineering, IEEE Transactions on, 27(8), 2107-2119.

DOI: 10.1109/tkde.2015.2399298