Estimation of obesity levels based on computational intelligence
Tài liệu tham khảo
Gutiérrez, 2010, Diez problemas de la Población de Jalisco: una perspectiva sociodemográfica
OMS, 2016
Olmedo, M. V. “La obesidad: un problema de salud pública. 2016 Revista de divulgación científica y tecnológica de la Universidad Veracruzana. Reference to a journal publication with an article number.
Hernández, 2011
Davila-Payan, 2015, Estimating prevalence of overweight or obese children and adolescents in small geographic areas using publicly available data, Prev Chronic Dis, 12, 10.5888/pcd12.140229
Manna, S., & Jewkes, A. M. “Understanding early childhood obesity risks: an empirical study using fuzzy signatures”, In Fuzzy systems (FUZZ-IEEE). 2014 IEEE international conference on (pp. 1333-1339). IEEE.
Adnan, 2012, A hybrid approach using Naïve Bayes and Genetic Algorithm for childhood obesity prediction”, vol. 1, 281
Adnan, 2011, A framework for childhood obesity classifications and predictions using NBtree”, 1
Adnan, M. H. B. M., Husain, W., & Damanhoori, F. “A survey on utilization of data mining for childhood obesity prediction”, In Information and telecommunication technologies (APSITT). 2010 8th asia-pacific symposium on (pp. 1-6). IEEE.
Dugan, 2015, Machine learning techniques for prediction of early childhood obesity, Appl Clin Inf, 6, 506, 10.4338/ACI-2015-03-RA-0036
Zhang, 2009, Multi-instance clustering with applications to multi-instance prediction, Appl Intell, 31, 47, 10.1007/s10489-007-0111-x
Suguna, 2016, Childhood obesity epidemic analysis using classification algorithms, Int. J. Mod. Comput. Sci, 4, 22
Abdullah, 2016, Data mining techniques for classification of childhood obesity among year 6 school children, 465
De-La-Hoz-Correa, 2019, Obesity level estimation software based on decision trees, J Comput Sci, 15, 67, 10.3844/jcssp.2019.67.77
Ward, 2016, Redrawing the US obesity landscape: bias-corrected estimates of state-specific adult obesity prevalence, PloS One, 11, 10.1371/journal.pone.0150735
Gómez, 2008, La obesidad: un factor de riesgo cardiometabólico, vol. 8, 91
Joachims, 1998, Text categorization with support vector machines
Kim, 2009, Human activity classification based on micro-Doppler signatures using a support vector machine”, IEEE Trans Geosci Rem Sens, 47, 1328, 10.1109/TGRS.2009.2012849
Da Silva F, Niedermeyer E. “Electroencephalography: basic principles. 1993. Clinical applications, and related fields”, William & Wikins, Baltimore.
Parsons, 2008, Affective outcomes of virtual reality exposure therapy for anxiety and specific phobias: a meta-analysis, J Behav Ther Exp Psychiatr, 39, 250, 10.1016/j.jbtep.2007.07.007
De la Hoz, 2014, Feature selection by multi-objective optimisation: application to network anomaly detection by hierarchical self-organising maps, Knowl-Based Syst, 71, 322, 10.1016/j.knosys.2014.08.013
Bekele, 2016, Multimodal adaptive social interaction in virtual environment (MASI-VR) for children with Autism spectrum disorders (ASD), 121
Han, 2001
Hastie, 2001
Xu, 2005, Survey of clustering algorithms, IEEE Trans Neural Network, 16, 645, 10.1109/TNN.2005.845141
Zhang, 2009, Multi-instance clustering with applications to multi-instance prediction, Appl Intell, 31, 47, 10.1007/s10489-007-0111-x
Palechor, 2017, Cardiovascular disease analysis using supervised and unsupervised data mining techniques, J SW, 12, 81
Mendoza-Palechor, 2018, Affective recognition from EEG signals: an integrated data-mining approach, Journal of Ambient Intelligence and Humanized Computing, 1
Fabio, 2017, Designing A method for alcohol consumption prediction based on clustering and support vector machines, Res J Appl Sci Eng Technol, 14, 146, 10.19026/rjaset.14.4158
Palechor, 2015, Feature selection, learning metrics and dimension reduction in training and classification processes in intrusion detection systems, J Theor Appl Inf Technol, 82, 291
Salley, 2016, Comparison between human and bite-based methods of estimating caloric intake, J Acad Nutr Diet, 116, 1568, 10.1016/j.jand.2016.03.007
Zhu, 2015, Using deep learning for energy expenditure estimation with wearable sensors, 501
Hall, 2017, Supervised machine‐learning reveals that old and obese people achieve low dapsone concentrations, CPT Pharmacometrics Syst Pharmacol, 6, 552, 10.1002/psp4.12208
Gerl, 2019, Machine learning of human plasma lipidomes for obesity estimation in a large population cohort, PLoS Biol, 17, 10.1371/journal.pbio.3000443
Craig, 2017