Cuckoo Search Optimized Reduction and Fuzzy Logic Classifier for Heart Disease and Diabetes Prediction

Disease forecasting using soft computing techniques is major area of research in data mining in recent years. To classify heart and diabetes diseases, this paper proposes a diagnosis system using cuckoo search optimized rough sets based attribute reduction and fuzzy logic system. The disease prediction is done as per the following steps 1 feature reduction using cuckoo search with rough set theory 2 Disease prediction using fuzzy logic system. The first step reduces the computational burden and enhances performance of fuzzy logic system. Second step is based on the fuzzy rules and membership functions which classifies the disease datasets. The authors have tested this approach on Cleveland, Hungarian, Switzerland heart disease data sets and a real-time diabetes dataset. The experimentation result demonstrates that the proposed algorithm outperforms the existing approaches.

[1]  A. Govardhan,et al.  Rough-Fuzzy Classifier: A System to Predict the Heart Disease by Blending Two Different Set Theories , 2014 .

[2]  Yu-Bin Yang,et al.  Lung cancer cell identification based on artificial neural network ensembles , 2002, Artif. Intell. Medicine.

[3]  Manjula Sanjay Koti,et al.  Knowledge Discovery in Medical Data by using Rough Set Rule Induction Algorithms , 2014 .

[4]  Katsumi Yoshida,et al.  A comparison between two neural network rule extraction techniques for the diagnosis of hepatobiliary disorders , 2000, Artif. Intell. Medicine.

[5]  E. Shortliffe.  Computer programs to support clinical decision making. , 1990, JAMA.

[6]  I. Jolliffe.  Principal Component Analysis , 2002 .

[7]  Giorgio Valentini,et al.  Cancer recognition with bagged ensembles of support vector machines , 2004, Neurocomputing.

[8]  Kailash D. Kharat,et al.  Brain Tumor Classification Using Neural Network Based Methods , 2012 .

[9]  Xin-She Yang,et al.  Cuckoo Search via Lévy flights , 2009, 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC).

[10]  M. Ataei,et al.  Heart diseases prediction based on ECG signals' classification using a genetic-fuzzy system and dynamical model of ECG signals , 2014, Biomed. Signal Process. Control..

[11]  A. Levine,et al.  Gene assessment and sample classification for gene expression data using a genetic algorithm/k-nearest neighbor method. , 2001, Combinatorial chemistry & high throughput screening.

[12]  D. Botstein,et al.  Cluster analysis and display of genome-wide expression patterns. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[13]  Z. Pawlak,et al.  Rough set approach to multi-attribute decision analysis , 1994 .

[14]  N. Yuvaraj,et al.  An efficient SVM based tumor classification with symmetry Non-negative Matrix Factorization using gene expression data , 2013, 2013 International Conference on Information Communication and Embedded Systems (ICICES).

[15]  Werner Dubitzky,et al.  Multiclass Cancer Classification Using Gene Expression Profiling and Probabilistic Neural Networks , 2002, Pacific Symposium on Biocomputing.

[16]  Phayung Meesad,et al.  A highly accurate firefly based algorithm for heart disease prediction , 2015, Expert Syst. Appl..

[17]  V. Seenivasagam,et al.  REVIEW OF HEART DISEASE PREDICTION SYSTEM USING DATA MINING AND HYBRID INTELLIGENT TECHNIQUES , 2013, SOCO 2013.

[18]  S. Dudoit,et al.  Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data , 2002 .

[19]  Neelu Khare,et al.  An Efficient System for Heart Disease Prediction Using Hybrid OFBAT with Rule-Based Fuzzy Logic Model , 2017, J. Circuits Syst. Comput..

[20]  Huanqing Feng,et al.  SDED: A novel filter method for cancer-related gene selection , 2008, Bioinformation.

[21]  Mohsen Beheshti,et al.  Diabetes Data Analysis and Prediction Model Discovery Using RapidMiner , 2008, 2008 Second International Conference on Future Generation Communication and Networking.

[22]  M. Cevdet Ince,et al.  An expert system for detection of breast cancer based on association rules and neural network , 2009, Expert Syst. Appl..

[23]  Dan Simon,et al.  Biogeography-based optimization of neuro-fuzzy system parameters for diagnosis of cardiac disease , 2010, GECCO '10.

[24]  G. Church,et al.  Systematic determination of genetic network architecture , 1999, Nature Genetics.

[25]  Igor Kononenko,et al.  Machine learning for medical diagnosis: history, state of the art and perspective , 2001, Artif. Intell. Medicine.

[26]  Paulo Carvalho,et al.  Prediction of Heart Failure Decompensation Events by Trend Analysis of Telemonitoring Data , 2015, IEEE Journal of Biomedical and Health Informatics.

[27]  T. Santhanam,et al.  Heart Disease Prediction Using Hybrid Genetic Fuzzy Model , 2015 .

[28]  G. Thippa Reddy,et al.  FFBAT-Optimized Rule Based Fuzzy Logic Classifier for Diabetes , 2016 .

[29]  M. Ringnér,et al.  Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks , 2001, Nature Medicine.

[30]  Chee Peng Lim,et al.  A hybrid intelligent system for medical data classification , 2014, Expert Syst. Appl..

[31]  Nurettin Acir,et al.  Automatic classification of auditory brainstem responses using SVM-based feature selection algorithm for threshold detection , 2006, Eng. Appl. Artif. Intell..

[32]  Alireza Osareh,et al.  A Computer Aided Diagnosis System for Breast Cancer , 2011 .

[33]  Yu Zhang,et al.  Automated defect recognition of C-SAM images in IC packaging using Support Vector Machines , 2005 .

[34]  Amir Hossein Gandomi,et al.  Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems , 2011, Engineering with Computers.

[35]  Z. Pawlak.  Rough Sets: Theoretical Aspects of Reasoning about Data , 1991 .

[36]  Kulthida Tuamsuk,et al.  Data Mining and Its Applications for Knowledge Management: A Literature Review from 2007 to 2012 , 2012, ArXiv.

[37]  Elena Marchiori,et al.  Ensemble Feature Ranking , 2004, PKDD.