A hybrid machine learning approach to cerebral stroke prediction based on imbalanced medical dataset
Tóm tắt
Từ khóa
Tài liệu tham khảo
Naghavi, 2017, Global, regional, and national age-sex specific mortality for 264 causes of death, 1980-2016: a systematic analysis for the global burden of disease study 2016, Lancet, 390, 1151, 10.1016/S0140-6736(17)32152-9
Algra, 2017, Stroke in 2016: Stroke is treatable but prevention is the key, Nat Rev Neurol, 13, 78, 10.1038/nrneurol.2017.4
O’Donnell, 2016, Global and regional effects of potentially modifiable risk factors associated with acute stroke in 32 countries (interstroke): a case-control study, Lancet, 388, 761, 10.1016/S0140-6736(16)30506-2
Yoo, 2012, Data mining in healthcare and biomedicine: a survey of the literature, J Med Syst, 36, 2431, 10.1007/s10916-011-9710-5
Richter, 2018, A review of statistical and machine learning methods for modeling cancer risk using structured clinical data, Artif Intell Med, 90, 1, 10.1016/j.artmed.2018.06.002
Pereira, 2018, A survey on computer-assisted parkinson's disease diagnosis, Artif Intell Med, 95, 48, 10.1016/j.artmed.2018.08.007
Kaya, 2018, Cascaded classifiers and stacking methods for classification of pulmonary nodule characteristics, Comput Methods Programs Biomed, 166, 77, 10.1016/j.cmpb.2018.10.009
Shishvan, 2018, Machine intelligence in healthcare and medical cyber physical systems: A survey, IEEE Access, 6, 46419, 10.1109/ACCESS.2018.2866049
Colak, 2015, Application of knowledge discovery process on the prediction of stroke, Comput Methods Programs Biomed, 119, 181, 10.1016/j.cmpb.2015.03.002
Khosla, 2010, An integrated machine learning approach to stroke prediction, 183
Kasabov, 2014, Evolving spiking neural networks for personalised modelling, classification and prediction of spatio-temporal patterns with a case study on stroke, Neurocomputing, 134, 269, 10.1016/j.neucom.2013.09.049
Arslan, 2016, Different medical data mining approaches based prediction of ischemic stroke, Comput Methods Programs Biomed, 130, 87, 10.1016/j.cmpb.2016.03.022
Jerez, 2010, Missing data imputation using statistical and machine learning methods in a real breast cancer problem, Artif Intell Med, 50, 105, 10.1016/j.artmed.2010.05.002
Hong, 2011, Mining rules from an incomplete dataset with a high missing rate, Expert Syst Appl, 38, 3931, 10.1016/j.eswa.2010.09.054
Zhang, 2013, Robust bayesian classification with incomplete data, Cogn Comput, 5, 170, 10.1007/s12559-012-9188-6
Haixiang, 2017, Learning from class-imbalanced data: Review of methods and applications, Expert Syst Appl, 73, 220, 10.1016/j.eswa.2016.12.035
Chawla, 2004, Editorial: special issue on learning from imbalanced data sets, SIGKDD Explor, 6, 1, 10.1145/1007730.1007733
Batista, 2004, A study of the behavior of several methods for balancing machine learning training data, ACM SIGKDD Explor Newsletter, 6, 20, 10.1145/1007730.1007735
Y.-M. Chyi, Classification analysis techniques for skewed class distribution problems, Department of Information Management, National Sun Yat-Sen University.
Chawla, 2002, Smote: synthetic minority over-sampling technique, J Artif Intell Res, 16, 321, 10.1613/jair.953
Lin, 2017, Clustering-based undersampling in class-imbalanced data, Inf Sci, 409, 17, 10.1016/j.ins.2017.05.008
Wang, 2019, A robust classification framework with mixture correntropy, Inf Sci, 491, 306, 10.1016/j.ins.2019.04.016
Yang, 2019, Robust support vector machine with generalized quantile loss for classification and regression, Appl Soft Comput, 81, 105483, 10.1016/j.asoc.2019.105483
Ghazikhani, 2013, Online cost-sensitive neural network classifiers for non-stationary and imbalanced data streams, Neural Comput Appl, 23, 1283, 10.1007/s00521-012-1071-6
Yu, 2015, Support vector machine-based optimized decision threshold adjustment strategy for classifying imbalanced data, Knowl-Based Syst, 76, 67, 10.1016/j.knosys.2014.12.007
Wang, 2019, A robust loss function for classification with imbalanced datasets, Neurocomputing, 331, 40, 10.1016/j.neucom.2018.11.024
Lin, 2017, Focal loss for dense object detection, Proc. IEEE Comput. Soc. Conf. Comput., 2980
Ozan, 2016, An optimized k-nn approach for classification on imbalanced datasets with missing data, 387
Liu, 2017, Fuzzy-based information decomposition for incomplete and imbalanced data learning, ?IEEE Trans Fuzzy Syst, 25, 1476, 10.1109/TFUZZ.2017.2754998
Leke, 2019, Introduction to missing data estimation, 1
Ben-Gal, 2005, Outlier detection, 131
Prasad, 2006, Newer classification and regression tree techniques: bagging and random forests for ecological prediction, Ecosystems, 9, 181, 10.1007/s10021-005-0054-1
Feurer, 2015, Efficient and robust automated machine learning, Adv Neural Inf Process Syst, 2962
Ding, 2004, K-means clustering via principal component analysis, 29
Kodinariya, 2013, Review on determining number of cluster in k-means clustering, International J, 1, 90
Tsai, 2019, Under-sampling class imbalanced datasets by combining clustering analysis and instance selection, Inf Sci, 477, 47, 10.1016/j.ins.2018.10.029
Ren, 2018, Learning to reweight examples for robust deep learning, Int Conf Mach Learn, 4331
Vladimir, 2013
Galar, 2012, A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches, IEEE T Syst Man Cy Part C, 42, 463, 10.1109/TSMCC.2011.2161285
Branco, 2016, A survey of predictive modeling on imbalanced domains, ACM Comput Surv, 49, 31, 10.1145/2907070