A bi-objective hybrid optimization algorithm to reduce noise and data dimension in diabetes diagnosis using support vector machines

Abdillah, 2016, Diagnosis of diabetes using support vector machines with radial basis function kernels, International Journal of Technology, 7.5, 849, 10.14716/ijtech.v7i5.1370 Ahmed, 2013, Intelligent medical disease diagnosis using improved hybrid genetic algorithm - multilayer perceptron network, Journal of Medical Systems, 37, 9934, 10.1007/s10916-013-9934-7 Ahmadi, 2016, A multi objective optimization approach for flexible job shop scheduling problem under random machine breakdown by evolutionary algorithms, Computers & Operations Research, 73, 56, 10.1016/j.cor.2016.03.009 Aladeemy, 2017, A new hybrid approach for feature selection and support vector machine model selection based on self-adaptive cohort intelligence, Expert Systems with Applications, 88, 118, 10.1016/j.eswa.2017.06.030 Alamaireh, 2006, A predictive neural network control approach in diabetes management by insulin administration, Vol. 1 Arul jeyaraj, 2015, A multi-objective placement of phasor measurement units considering observability and measurement redundancy using firefly algorithm, Journal of Electrical Engineering and Technology, 10, 474, 10.5370/JEET.2015.10.2.474 Atashpaz-Gargari, 2007, Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition Banati, 2011, Firefly based feature selection approach, International Journal of Computer Science Issues, 8, 473 Bose, 2008, Data mining in diabetes diagnosis and detection, 1817 Burges, 1998, A tutorial on support vector machines for pattern recognition, Data Mining and Knowledge Discovery, 2.2, 121, 10.1023/A:1009715923555 Carter, 2019, Combining elemental analysis of toenails and machine learning techniques as a non-invasive diagnostic tool for the robust classification of type-2 diabetes, Expert Systems with Applications, 115, 245, 10.1016/j.eswa.2018.08.002 Chandrashekar, 2014, A survey on feature selection methods, Computers & Electrical Engineering, 40.1, 16, 10.1016/j.compeleceng.2013.11.024 Cordella, 2008, A feature selection algorithm for handwritten character recognition Cunha, 2018, Stem-cell-based therapies for improving islet transplantation outcomes in type 1 diabetes, Current Diabetes Reviews, 14, 3, 10.2174/1573399812666160629094031 De Stefano, 2007, A feature selection algorithm for class discrimination improvement Deb, 2002, A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Transactions on Evolutionary Computation, 6, 182, 10.1109/4235.996017 Eberhart, 1995, A new optimizer using particle swarm theory García, 2016, Big data preprocessing: Methods and prospects, Big Data Analytics, 1.9, 1 Gheyas, 2010, Feature subset selection in large dimensionality domains, Pattern Recognition, 43, 5, 10.1016/j.patcog.2009.06.009 Goldberg, 1998, Genetic algorithms and machine learning, Machine Learning, 3, 95, 10.1023/A:1022602019183 Gupta, 2017, Local search methods for k-means with outliers, Proceedings of the VLDB Endowment, 10, 757, 10.14778/3067421.3067425 Gyamfi, 2018, Linear dimensionality reduction for classification via a sequential Bayes error minimisation with an application to flow meter diagnostics, Expert Systems with Applications, 91, 252, 10.1016/j.eswa.2017.09.010 Holland, 1973, Genetic algorithms and the optimal allocation of trials, SIAM Journal of Computing, 2, 88, 10.1137/0202009 Houari, 2016, Dimensionality reduction in data mining: A Copula approach, Expert Systems with Applications, 64, 247, 10.1016/j.eswa.2016.07.041 Huang, 2007, Feature selection and classification model construction on type 2 diabetic patients’ data, Artificial Intelligence in Medicine, 41, 251, 10.1016/j.artmed.2007.07.002 2018 James, 2013, Vol. 112 Kapur, 2007, Economic analysis of diabetes care, Indian Journal of Medical Research, 125, 473 Karatsiolis, 2012, Region based support vector machine algorithm for medical diagnosis on Pima Indian diabetes dataset Kim, 2015, Mind the gap: A generative approach to interpretable feature selection and extraction Kumar, 2014, An efficient feature selection system to integrating SVM with genetic algorithm for large medical datasets, International Journal of Advanced Research Computer Science and Software Engineering Research, 4, 272 Kumari, 2013, Classification of diabetes disease using support vector machine, International Journal of Engineering Research and Applications, 3, 1797 Lee, 2012, Predictors of diabetes remission after bariatric surgery in Asia, Asian Journal of Surgery, 35, 67, 10.1016/j.asjsur.2012.04.010 Maghsoudlou, 2017, Multi-skilled project scheduling with level-dependent rework risk; three multi-objective mechanisms based on cuckoo search, Applied Soft Computing, 54, 46, 10.1016/j.asoc.2017.01.024 Manikandan, 2018, A survey on feature selection and extraction techniques for high-dimensional microarray datasets, 311 Miyaki, 2002, Novel statistical classification model of type 2 diabetes mellitus patients for tailormade prevention using data mining algorithm, Journal of Epidemiology, 12, 243, 10.2188/jea.12.243 Mousavirad, 2013, Feature selection using modified imperialist competitive algorithm Repalli, 2011 Rudd, 2018, Application of support vector machine modeling and graph theory metrics for disease classification, Model Assisted Statistics and Applications, 13.4, 341, 10.3233/MAS-180444 Santhanam, 2015, Application of K-means and genetic algorithms for dimension reduction by integrating SVM for diabetes diagnosis, Procedia Computer Science, 47, 76, 10.1016/j.procs.2015.03.185 Seera, 2014, A hybrid intelligent system for medical data classification, Expert Systems with Applications, 41, 2239, 10.1016/j.eswa.2013.09.022 Shanker, 1996, Using neural networks to predict the onset of diabetes mellitus, Journal of Chemical Information and Computer Sciences, 36, 35, 10.1021/ci950063e Sohrabi, 2017, Multi-objective feature selection for warfarin dose prediction, Computational Biology and Chemistry, 69, 126, 10.1016/j.compbiolchem.2017.06.002 Swapna, 2017, A critical study on cluster analysis methods to extract liver disease patterns in Indian liver patient data, International Journal of Computational Intelligence Research, 13, 2379 Taguchi, 1986 Talbi, 2008, Comparison of population based metaheuristics for feature selection: Application to microarray data classification Tambade, 2017, SVM based diabetic classification and hospital recommendation, International Journal of Computer Applications, 167.1, 40, 10.5120/ijca2017914141 Wang, 2007, Feature selection based on rough sets and particle swarm optimization, Pattern Recognition Letters, 28, 459, 10.1016/j.patrec.2006.09.003 2018 Xiao, 2017, Davies Bouldin Index based hierarchical initialization K-means, Intelligent Data Analysis, 21, 1327, 10.3233/IDA-163129 Xue, 2012, Multi-objective particle swarm optimization (PSO) for feature selection Yang, 2008 Yilmaz, 2014, A new data preparation method based on clustering algorithms for diagnosis systems of heart and diabetes diseases, Journal of Medical Systems, 38, 1, 10.1007/s10916-014-0048-7 Yu, 2018, Evaluating multiobjective evolutionary algorithms using MCDM methods, Mathematical Problems in Engineering, 10.1155/2018/9751783