Classification and features selection method for obesity level prediction

Obesity has become one of the world’s largest health issues, rich and poor countries, without exception, have each year larger populations with this condition. Obesity and overweight are defined as abnormal or excessive fat accumulation that may impair health according to the World Health Organizati...

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Autores:
Molina Estren, Diego
De la Hoz Manotas, Alexis Kevin
Mendoza Palechor, Fabio
Tipo de recurso:
Article of journal
Fecha de publicación:
2021
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/8417
Acceso en línea:
https://hdl.handle.net/11323/8417
https://repositorio.cuc.edu.co/
Palabra clave:
Data mining
Dataset
Obesity
Decision Trees
Support Vector Machines
Rights
openAccess
License
Attribution-NonCommercial-NoDerivatives 4.0 International
id RCUC2_5943f0a77ef88685e2e90278f2ad8534
oai_identifier_str oai:repositorio.cuc.edu.co:11323/8417
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv Classification and features selection method for obesity level prediction
title Classification and features selection method for obesity level prediction
spellingShingle Classification and features selection method for obesity level prediction
Data mining
Dataset
Obesity
Decision Trees
Support Vector Machines
title_short Classification and features selection method for obesity level prediction
title_full Classification and features selection method for obesity level prediction
title_fullStr Classification and features selection method for obesity level prediction
title_full_unstemmed Classification and features selection method for obesity level prediction
title_sort Classification and features selection method for obesity level prediction
dc.creator.fl_str_mv Molina Estren, Diego
De la Hoz Manotas, Alexis Kevin
Mendoza Palechor, Fabio
dc.contributor.author.spa.fl_str_mv Molina Estren, Diego
De la Hoz Manotas, Alexis Kevin
Mendoza Palechor, Fabio
dc.subject.spa.fl_str_mv Data mining
Dataset
Obesity
Decision Trees
Support Vector Machines
topic Data mining
Dataset
Obesity
Decision Trees
Support Vector Machines
description Obesity has become one of the world’s largest health issues, rich and poor countries, without exception, have each year larger populations with this condition. Obesity and overweight are defined as abnormal or excessive fat accumulation that may impair health according to the World Health Organization (WHO) and has nearly tripled since 1975. Data Mining and their techniques have become a strong scientific field to analyze huge data sources and to provide new information about patterns and behaviors from the population. This study uses data mining techniques to build a model for obesity prediction, using a dataset based on a survey for college students in several countries. After cleaning and transformation of the data, a set of classification methods was implemented (Logistic Model Tree - LMT, RandomForest - RF, Multi-Layer Perceptron - MLP and Support Vector Machines - SVM), and the feature selection methods InfoGain, GainRatio, Chi-Square and Relief, finally, crossed validation was performed for the training and testing processes. The data showed than LMT had the best performance in precision, obtaining 96.65%, compared to RandomForest (95.62%), MLP (94.41%) and SMO (83.89%), so this study shows that LMT it can be used with confidence to analyze obesity and similar data.
publishDate 2021
dc.date.accessioned.none.fl_str_mv 2021-06-26T16:42:54Z
dc.date.available.none.fl_str_mv 2021-06-26T16:42:54Z
dc.date.issued.none.fl_str_mv 2021-06-15
dc.type.spa.fl_str_mv Artículo de revista
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_6501
dc.type.content.spa.fl_str_mv Text
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/article
dc.type.redcol.spa.fl_str_mv http://purl.org/redcol/resource_type/ART
dc.type.version.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
format http://purl.org/coar/resource_type/c_6501
status_str acceptedVersion
dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/8417
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
url https://hdl.handle.net/11323/8417
https://repositorio.cuc.edu.co/
identifier_str_mv Corporación Universidad de la Costa
REDICUC - Repositorio CUC
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.references.spa.fl_str_mv WHO, «Obesity and overweight,» 2020. [En línea]. Available: https://www.who.int/newsroom/fact-sheets/detail/obesity-andoverweight.
O. W. i. Data, «Obesity - Our World In Data,» 2020. [En línea]. Available: https://ourworldindata.org/obesity#:~:text=13 %25%20of%20adults%20in%20the,of%20en ergy%20intake%20and%20expenditure..
Statista, «Overweight prevalence by age,» 2020. [En línea]. Available: https://www.statista.com/statistics/1065605/p revalence-overweight-people-worldwide-byage/.
C. Davila-Payan, M. DeGuzman, K. Jhonson, N. Serban y J. Swann, «Estimating prevalence of overweight or obese children and adolescents in small geographic areas using publicly available data,» Preventing Chronic Disease, vol. 12, 2015.
S. Manna y A. Jewkes, «Understanding early childhood obesity risks: An empirical study using fuzzy signatures,» 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2014.
T. Dugan, S. Mukhopadhyay, A. Carroll y S. Downs, «Machine learning techniques for prediction of early childhood obesity,» Applied Clinical Informatics, 2015.
M. H. Muhammad Adnan, W. Husain y N. Abdul Rashi, «A hybrid approach using Naïve Bayes and Genetic Algorithm for childhood obesity prediction,» 2012 International Conference on Computer & Information Science (ICCIS), 2012.
M. H. Muhammad Adnan, W. Husain y N. Abdul Rashi, «A framework for childhood obesity classifications and predictions using NBTree.,» Information Technology in Asia (CITA 11), pp. 1-6, 2011.
M. H. Muhammad Adnan, W. Husain y F. Damanhoori, «A survey on utilization of data mining for childhood obesity prediction,» Information and Telecommunication Technologies (APSITT), pp. 1-6, 2010.
S. Zhang, C. Tjortjis, X. Zeng, H. Qiao, I. Buchan y J. Keane, «Comparing data mining methods with logistic regression in childhood obesity prediction,» Information Systems Frontiers, pp. 449-460, 2009.
M. Suguna, «Childhood obesity epidemic analysis using classification algorithms,» Int. J. Mod. Comput. Sci, Vols. %1 de %222-26, 2016.
F. Abdullah, N. Manan, A. Ahmad, S. Wafa, M. Shahril, N. Zulaily y A. Ahmed, «Data Mining Techniques for Classification of Childhood Obesity Among Year 6 School Children,» International Conference on Soft Computing and Data Mining, pp. 465-474, 2016.
E. De-La-Hoz-Correa, F. Mendoza-Palechor, A. De-la-Hoz-Manotas, R. Morales-Ortega y B. Sanchez Hernandez, «Obesity Level Estimation Software based on Decision Trees,» Journal of Computer Science, vol. 15, 2019.
A. Aora, «Obesity among adults by country, 1975-2016,» 2020. [En línea]. Available: https://www.kaggle.com/amanarora/obesityamong-adults-by-country-19752016.
Eurostat, «Obesity rate by Body Mass Index,» 2020. [En línea]. Available: https://data.europa.eu/euodp/en/data/dataset/ A2eMGcMJTMLVVWbsvAlr8w.
Center for Disease Control and Prevention, «Nutrition, Physical Activity, and Obesity - Behavioral risk factor surveillance system,» 2020. [En línea]. Available: https://healthdata.gov/dataset/nutritionphysical-activity-and-obesity-behavioralrisk-factor-surveillance-system.
R. Hossain, S. Hasan, M. A. Hossin, S. R. Haider Noori y H. Jahan, «PRMT: Predicting risk factor of obesity among middle-aged people using data mining techniques,» Procedia Computer Sciences, vol. 132, pp. 1068-1076, 2018.
R. Cañas Cervantes y U. Martinez Palacio, «Estimation of obesity levels based on computational intelligence,» Informatics in Medicine Unlocked, 2020.
S. Akben, «Determination of the Blood, Hormone and Obesity Value Ranges that Indicate the Breast Cancer, Using Data Mining Based Expert System,» IRBM, vol. 40, 2019.
S. Ping y M. Goodson, «A Data Mining Approach Identified Salivary Biomarkers That Discriminate between Two Obesity Measures,» Journal of Obesity, 2019.
A. Joshi, T. Choudhury, A. S. Sabitha y K. Srujan Raju, «Data Mining in Healthcare and Predicting Obesity,» Proceedings of the Third International Conference on Computational Intelligence and Informatics, vol. 1090, 2020.
M. Siddiqui, R. Morales-Menendez y A. Sultan, «Application of Receiver Operating Characteristics (ROC) on the Prediction of Obesity,» Brazilian Archives of Biology and Technology, vol. 63, 2020.
C. Kim y S. Youm, «Development of a Web Application Based on Human Body Obesity Index and Self-Obesity Diagnosis Model Using the Data Mining Methodology,» Sustainability, vol. 12, 2020.
N. A. Daud, N. L. Mohd Noor, S. Aljunid, N. Noordin y N. I. Fahmi Teng, «Predictive Analytics: The Application of J48 Algorithm on Grocery Data to Predict Obesity,» 2018 IEEE Conference on Big Data and Analytics (ICBDA), 2018.
A. Bu y L. Wang, «Research on the Rule of Acupuncture and Moxibustion for Treatment of Obesity Based on Data Mining,» 2016 International Conference on Smart City and Systems Engineering (ICSCSE), 2016.
H. Sharma, C. Mao, Y. Zhang, H. Vatani, L. Yao, Y. Zhong, L. Rasmussen, G. Jiang, J. Pathak y Y. Luo, «Portable Phenotyping System: A Portable Machine-Learning Approach to i2b2 Obesity Challenge,» 2018 IEEE International Conference on Healthcare Informatics Workshop (ICHI-W), 2018.
N. Nadar Selvin y A. Srinivasaragahavan, «Dimensionality reduction of inputs for a Fuzzy Cognitive Map for obesity problem,» 2016 International Conference on Inventive Computation Technologies (ICICT), 2016.
C. Curbelo, P. Fergus, C. Chalmers, N. Hassain Malim, B. Abdulaimma, D. Reilly y F. Falciani, «SAERMA: Stacked Autoencoder Rule Mining Algorithm for the Interpretation of Epistatic Interactions in GWAS for Extreme Obesity,» IEEE Access, 2020.
A. Ortega Hinojosa, M. Davies, S. Jarjour, R. Burnett, J. Mann, E. Hughes, J. Balmes, M. Turner y M. Jerrett, «Developing small-area predictions for smoking and obesity prevalence in the United States for use in Environmental Public Health Tracking,» Environmental Research, 2014.
C. Lazarou, M. Karaolis, A.-L. Matalas y D. Panagiotakos, «Dietary patterns analysis using data mining method. An application to data from the CYKIDS study,» Computer Methods and Programas in Biomedicine, 2012.
A. Pochini, Y. Wu y G. Hu, «Data Mining for Lifestyle Risk Factors Associated with Overweight and Obesity among Adolescents,» Data Mining for Lifestyle Risk Factors Associated with Overweight and Obesity among Adolescents, 2014.
H. Jung y K. Chung, «Knowledge-based dietary nutrition recommendation for obese management,» Information Technology and Management, 2016.
S. Harous, M. A. Serhani, M. El Menshawy y A. Benharref, «Hybrid obesity monitoring model using sensors and community engagement,» 2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC), 2017.
R. Salehnejad, R. Allmendiger, Y.-W. Chen, M. Ali, A. Shahgholian, P. Yiapanis y M. Mansur, «Leveraging data mining techniques to understand drivers of obesity,» 2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2017.
M. Firman Maulana y M. Defriani, «Logistic Model Tree and Decision Tree J48 Algorithms for predicting the length of study period,» Journal Penelitian Ilmu Komputer, System Embedded & Logic, vol. 8, pp. 39-48, 2020.
N. Landwehr, M. Hall y E. Frank, «Logistic Model Trees,» Machine Learning, vol. 59, 2005.
M. Friedl y C. Brodley, «Decision tree classification of landcover from remotely sensed data,» Remote sensing of environment, vol. 61, nº 3, pp. 399-409, 1997.
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spelling Molina Estren, DiegoDe la Hoz Manotas, Alexis KevinMendoza Palechor, Fabio2021-06-26T16:42:54Z2021-06-26T16:42:54Z2021-06-15https://hdl.handle.net/11323/8417Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Obesity has become one of the world’s largest health issues, rich and poor countries, without exception, have each year larger populations with this condition. Obesity and overweight are defined as abnormal or excessive fat accumulation that may impair health according to the World Health Organization (WHO) and has nearly tripled since 1975. Data Mining and their techniques have become a strong scientific field to analyze huge data sources and to provide new information about patterns and behaviors from the population. This study uses data mining techniques to build a model for obesity prediction, using a dataset based on a survey for college students in several countries. After cleaning and transformation of the data, a set of classification methods was implemented (Logistic Model Tree - LMT, RandomForest - RF, Multi-Layer Perceptron - MLP and Support Vector Machines - SVM), and the feature selection methods InfoGain, GainRatio, Chi-Square and Relief, finally, crossed validation was performed for the training and testing processes. The data showed than LMT had the best performance in precision, obtaining 96.65%, compared to RandomForest (95.62%), MLP (94.41%) and SMO (83.89%), so this study shows that LMT it can be used with confidence to analyze obesity and similar data.Molina Estren, Diego-will be generated-orcid-0000-0003-4084-7567-600De la Hoz Manotas, Alexis Kevin-will be generated-orcid-0000-0002-8328-1076-600Mendoza Palechor, Fabio-will be generated-orcid-0000-0002-2755-0841-600application/pdfengAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Journal of Theoretical and Applied Information Technologyhttp://www.jatit.org/volumes/Vol99No11/3Vol99No11.pdfData miningDatasetObesityDecision TreesSupport Vector MachinesClassification and features selection method for obesity level predictionArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersionWHO, «Obesity and overweight,» 2020. [En línea]. Available: https://www.who.int/newsroom/fact-sheets/detail/obesity-andoverweight.O. W. i. Data, «Obesity - Our World In Data,» 2020. [En línea]. Available: https://ourworldindata.org/obesity#:~:text=13 %25%20of%20adults%20in%20the,of%20en ergy%20intake%20and%20expenditure..Statista, «Overweight prevalence by age,» 2020. [En línea]. Available: https://www.statista.com/statistics/1065605/p revalence-overweight-people-worldwide-byage/.C. Davila-Payan, M. DeGuzman, K. Jhonson, N. Serban y J. Swann, «Estimating prevalence of overweight or obese children and adolescents in small geographic areas using publicly available data,» Preventing Chronic Disease, vol. 12, 2015.S. Manna y A. Jewkes, «Understanding early childhood obesity risks: An empirical study using fuzzy signatures,» 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2014.T. Dugan, S. Mukhopadhyay, A. Carroll y S. Downs, «Machine learning techniques for prediction of early childhood obesity,» Applied Clinical Informatics, 2015.M. H. Muhammad Adnan, W. Husain y N. Abdul Rashi, «A hybrid approach using Naïve Bayes and Genetic Algorithm for childhood obesity prediction,» 2012 International Conference on Computer & Information Science (ICCIS), 2012.M. H. Muhammad Adnan, W. Husain y N. Abdul Rashi, «A framework for childhood obesity classifications and predictions using NBTree.,» Information Technology in Asia (CITA 11), pp. 1-6, 2011.M. H. Muhammad Adnan, W. Husain y F. Damanhoori, «A survey on utilization of data mining for childhood obesity prediction,» Information and Telecommunication Technologies (APSITT), pp. 1-6, 2010.S. Zhang, C. Tjortjis, X. Zeng, H. Qiao, I. Buchan y J. Keane, «Comparing data mining methods with logistic regression in childhood obesity prediction,» Information Systems Frontiers, pp. 449-460, 2009.M. Suguna, «Childhood obesity epidemic analysis using classification algorithms,» Int. J. Mod. Comput. Sci, Vols. %1 de %222-26, 2016.F. Abdullah, N. Manan, A. Ahmad, S. Wafa, M. Shahril, N. Zulaily y A. Ahmed, «Data Mining Techniques for Classification of Childhood Obesity Among Year 6 School Children,» International Conference on Soft Computing and Data Mining, pp. 465-474, 2016.E. De-La-Hoz-Correa, F. Mendoza-Palechor, A. De-la-Hoz-Manotas, R. Morales-Ortega y B. Sanchez Hernandez, «Obesity Level Estimation Software based on Decision Trees,» Journal of Computer Science, vol. 15, 2019.A. Aora, «Obesity among adults by country, 1975-2016,» 2020. [En línea]. Available: https://www.kaggle.com/amanarora/obesityamong-adults-by-country-19752016.Eurostat, «Obesity rate by Body Mass Index,» 2020. [En línea]. Available: https://data.europa.eu/euodp/en/data/dataset/ A2eMGcMJTMLVVWbsvAlr8w.Center for Disease Control and Prevention, «Nutrition, Physical Activity, and Obesity - Behavioral risk factor surveillance system,» 2020. [En línea]. Available: https://healthdata.gov/dataset/nutritionphysical-activity-and-obesity-behavioralrisk-factor-surveillance-system.R. Hossain, S. Hasan, M. A. Hossin, S. R. Haider Noori y H. Jahan, «PRMT: Predicting risk factor of obesity among middle-aged people using data mining techniques,» Procedia Computer Sciences, vol. 132, pp. 1068-1076, 2018.R. Cañas Cervantes y U. Martinez Palacio, «Estimation of obesity levels based on computational intelligence,» Informatics in Medicine Unlocked, 2020.S. Akben, «Determination of the Blood, Hormone and Obesity Value Ranges that Indicate the Breast Cancer, Using Data Mining Based Expert System,» IRBM, vol. 40, 2019.S. Ping y M. Goodson, «A Data Mining Approach Identified Salivary Biomarkers That Discriminate between Two Obesity Measures,» Journal of Obesity, 2019.A. Joshi, T. Choudhury, A. S. Sabitha y K. Srujan Raju, «Data Mining in Healthcare and Predicting Obesity,» Proceedings of the Third International Conference on Computational Intelligence and Informatics, vol. 1090, 2020.M. Siddiqui, R. Morales-Menendez y A. Sultan, «Application of Receiver Operating Characteristics (ROC) on the Prediction of Obesity,» Brazilian Archives of Biology and Technology, vol. 63, 2020.C. Kim y S. Youm, «Development of a Web Application Based on Human Body Obesity Index and Self-Obesity Diagnosis Model Using the Data Mining Methodology,» Sustainability, vol. 12, 2020.N. A. Daud, N. L. Mohd Noor, S. Aljunid, N. Noordin y N. I. Fahmi Teng, «Predictive Analytics: The Application of J48 Algorithm on Grocery Data to Predict Obesity,» 2018 IEEE Conference on Big Data and Analytics (ICBDA), 2018.A. Bu y L. Wang, «Research on the Rule of Acupuncture and Moxibustion for Treatment of Obesity Based on Data Mining,» 2016 International Conference on Smart City and Systems Engineering (ICSCSE), 2016.H. Sharma, C. Mao, Y. Zhang, H. Vatani, L. Yao, Y. Zhong, L. Rasmussen, G. Jiang, J. Pathak y Y. Luo, «Portable Phenotyping System: A Portable Machine-Learning Approach to i2b2 Obesity Challenge,» 2018 IEEE International Conference on Healthcare Informatics Workshop (ICHI-W), 2018.N. Nadar Selvin y A. Srinivasaragahavan, «Dimensionality reduction of inputs for a Fuzzy Cognitive Map for obesity problem,» 2016 International Conference on Inventive Computation Technologies (ICICT), 2016.C. Curbelo, P. Fergus, C. Chalmers, N. Hassain Malim, B. Abdulaimma, D. Reilly y F. Falciani, «SAERMA: Stacked Autoencoder Rule Mining Algorithm for the Interpretation of Epistatic Interactions in GWAS for Extreme Obesity,» IEEE Access, 2020.A. Ortega Hinojosa, M. Davies, S. Jarjour, R. Burnett, J. Mann, E. Hughes, J. Balmes, M. Turner y M. Jerrett, «Developing small-area predictions for smoking and obesity prevalence in the United States for use in Environmental Public Health Tracking,» Environmental Research, 2014.C. Lazarou, M. Karaolis, A.-L. Matalas y D. Panagiotakos, «Dietary patterns analysis using data mining method. An application to data from the CYKIDS study,» Computer Methods and Programas in Biomedicine, 2012.A. Pochini, Y. Wu y G. Hu, «Data Mining for Lifestyle Risk Factors Associated with Overweight and Obesity among Adolescents,» Data Mining for Lifestyle Risk Factors Associated with Overweight and Obesity among Adolescents, 2014.H. Jung y K. Chung, «Knowledge-based dietary nutrition recommendation for obese management,» Information Technology and Management, 2016.S. Harous, M. A. Serhani, M. El Menshawy y A. Benharref, «Hybrid obesity monitoring model using sensors and community engagement,» 2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC), 2017.R. Salehnejad, R. Allmendiger, Y.-W. Chen, M. Ali, A. Shahgholian, P. Yiapanis y M. Mansur, «Leveraging data mining techniques to understand drivers of obesity,» 2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2017.M. Firman Maulana y M. Defriani, «Logistic Model Tree and Decision Tree J48 Algorithms for predicting the length of study period,» Journal Penelitian Ilmu Komputer, System Embedded & Logic, vol. 8, pp. 39-48, 2020.N. Landwehr, M. Hall y E. Frank, «Logistic Model Trees,» Machine Learning, vol. 59, 2005.M. Friedl y C. Brodley, «Decision tree classification of landcover from remotely sensed data,» Remote sensing of environment, vol. 61, nº 3, pp. 399-409, 1997.D. 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Qiao, «Construction of Metabolism prediction models for CYP450s, 3A4, 2D6 and 2C9 based on microsomal metabolic reaction system».PublicationORIGINAL3Vol99No11.pdf3Vol99No11.pdfapplication/pdf504234https://repositorio.cuc.edu.co/bitstreams/4fa3da25-18ad-43bf-b7c8-41e1d5e00fc4/downloadfb179c2cabbfba9f658da09509f004f9MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://repositorio.cuc.edu.co/bitstreams/5a13a9cb-651a-4c73-9cde-e3e1c7554a55/download4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; 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