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Continuous coordinated efforts between Heart specialists and researchers are taking a step ahead for utilization of information mining strategies to the field of individual patient health determination based on clinical records. In this unique situation, this proposed work introduces the utilization of instance acknowledgment and information mining procedures into identification of heart diseases. The information is demonstrated and ordered by utilizing information mining procedures with some constraints in both supervised and unsupervised learning strategies. These constraints are overcome in this examination by utilization of Fuzzy neural system models which can certainly recognize complex nonlinear connections amongst needy and open factors and the capacity to distinguish every single conceivable cooperation between indicator factors. The proposed work recommends that a selection sensitively supports system for Heart solution that can be assembled using the proposed disease prediction models and characterization methods and can be stretched out for other medicinal spaces. A specific concentration is the utilization of Fuzzy Neural Genetic Algorithm (FNGA) and the utilization of mining techniques to create a novel prescient model for use in the Heart Disease Prediction area.
data mining, heart information, fuzzy neural systems, bunching, risk prediction
[1] Adidela Daveedu R, Navya Sri M, Lavanya Devi G. (2016). Attribute subset selection by mixed weighting mean classification method. Proceedings of the International Conference on Electrical, Electronics, and Optimization Techniques 3523-3531.
[2] Kibler AD, Albert MK. (1991). Instance-based learning algorithms. Machine Learning 6(1): 37–66.
[3] Ahmad TA, Bouaynaya N, Polikar R. (2015). Inductive learning based on rough set theory for medical decision making. Fuzzy Systems (FUZZ-IEEE).
[4] Akhil JM, Chandra P, Deekshatulu BL. (2013). Classification of heart disease using artificial neural network and feature subset selection. Global Journal of Computer Science and Technology 13(3): 5-14.
[5] Akhil Jabbar M, Chandra P, Deekshatulu BL. (2012). Heart disease prediction system using associative classification and genetic algorithm. Proceedings of the International Conference on Emerging trends in Electrical, Electronics and Communication Technologies- ICECIT.
[6] Adeli A, Neshat M. (2010). A fuzzy expert system for heart disease diagnosis. Proceedings of the International Multiconference of Engineers and Computer Scientists I.
[7] Mahdavi-Shahri A, Mahboobeh H, Yaghoobi M, Jalali M (2016). Applying an ensemble learning method for improving multi-label classification performance. ICSPIS.
[8] Anbarasi M. (2010). Enhanced prediction of heart disease with feature subset selection using genetic algorithm. International Journal of Engineering Science and Technology 2(10): 5370-5376.
[9] Asha Gowda Karegowda ASM, Jayaram MA. (2010). Comparative study of attribute selection using gain ratio and correlation based feature selection. International Journal of Information Technology and Knowledge Management 2(2): 271-277.
[10] Jabar AL, Rashid TA. (2015). Combining fuzzy rough set with salient features for hrm classification. Proceedings of the International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing 244-251.
[11] Fawzy A, Mokhtar HMO, Hegazy O. (2013). Outliers detection and classification in wireless sensor networks. Egyptian Informatics Journal 14(2): 157-164.
[12] Baldwin. JF. (1978). Fuzzy logic and fuzzy reasoning. International Journal of Man-Machine Studies 11: 465-480.
[13] Beant K, Williamjeet S. (2014). Review on heart disease prediction system using data mining techniques. International Journal on Recent and Innovation Trends in Computing and Communication 2(10): 3003-3008.
[14] Ben-Gal I, Maimon O, Rockach L. (2005). Data mining and knowledge discovery handbook: a complete guide for practitioners and researchers. Kluwer Academic Publishers, ISBN 0-387-24435-2.
[15] Fong Tsai C, Lin CY. (2010). A triangle based nearest neighbor approach to intrusion detection. Pattern Recognition 43: 222-229.
[16] Chitra R, Seenivasagam V. (2013). Review of heart disease prediction system using data mining and hybrid intelligent techniques. Journal on Soft Computing 3(4): 605-609.
[17] Peña D, Prieto FJ. (2001). Multivariate outlier detection and robust covariance matrix estimation. Technometrics 43(3): 288-310.
[18] Sathiaraj D, Triantaphyllou E. (2013). On identifying critical nuggets of information during classification tasks. IEEE Transactions on Knowledge and Data Engineering 25(6): 1354-1367.
[19] Deekshatulu.BL, Akhil Jabbar M, Chandra P. (2013). Classification of heart disease using artificial neural network. Global Journal of Computer Science and Technology Neural & Artificial Intelligence 13(3): 5-10.
[20] Dhanwani D, Wadhe A. (2013). Study of hybrid genetic algorithm using artificial neural networks in data mining for the diagnosis of stroke disease. International Journal of Computational Engineering Research 3(4): 95-100.
[21] Chandna D. (2014). Diagnosis of heart disease using data mining algorithm. International Journal of Computer Science and Information Technologies 5(2): 1678-1680.
[22] Moorthi DKM. (2014). A novel approach for identifying critical nuggets using PCA with ANOVA. International Journal of Computer Science and Information Technology Research 2(4): 271-278.
[23] Dhivya S, Merlin Mercy E (2016). Heart disease classification and its co-morbid condition detection using WPCA genetic algorithm. International Journal of Multidisciplinary Research and Development 3(4): 287-291.
[24] Chao DF, Peng T, Snooke N, Shen Q. (2014). Feature selection inspired classifier ensemble reduction. IEEE Transactions on Cybernetics 44(8): 1259–1268.
[25] Armento E, Vluymans S, Verbiest N, Caballero Y, Bello R, Cornelis C, Herrera F. (2014). IFROWANN: Imbalanced fuzzy- rough ordered weighted average nearest neighbor classification. IEEE Transactions on Fuzzy Systems 23(5): 1622–1637.
[26] Filzmoser P. (2005). Identification of multivariate outliers: A performance study. Austrian Journal of Statistics 34(2): 127–138.
[27] Thomas GS, Budhkar SS, Cheulkar SK, Choudhary AB, Rohan S. (2015). Heart disease diagnosis system using apriori algorithm. International Journal of Advanced Research in Computer Science and Software Engineering 5(2): 430– 432.
[28] Chen GJ, Zhang XY, Wang ZZJ, Li FL. (2015). Robust support vector data description for outlier detection with noise or uncertain data. Knowledge-Based Systems, Elsevier 90: 129-137.