Obesity Level Estimation Software based on Decision Trees

De-La-Hoz-Correa, Eduardo; Mendoza-Palechor, Fabio E.; De-La-Hoz-Manotas, Alexis; Morales-Ortega, Roberto Cesar; Adriana, Sánchez Hernández Beatriz

doi:10.3844/jcssp.2019.67.77

Cited by 33 publications

(24 citation statements)

References 15 publications

(21 reference statements)

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“…To further verify the effect of the regression rates, we applied the data mining methods to another medical dataset made of electronic health records of young patients with obesity ( Palechor & De-La-Hoz-Manotas, 2019 , De-La-Hoz-Correa et al, 2019 ). This dataset is publicly available in the University of California Irvine Machine Learning Repository (2019) too, and contains data of 2,111 individuals, with 17 variables for each of them.…”

Section: Resultsmentioning

confidence: 99%

“…In this dataset, there are 272 children with insufficient weight (12.88%), 287 children with normal weight (13.6%), 351 children with obesity type I (16.63%), 297 children with obesity type II (14.07%), 324 children with obesity type III (15.35%), 290 children with overweight level I (13.74%), and 290 children with overweight level II (13.74%). The original curators synthetically generated part of this dataset ( Palechor & De-La-Hoz-Manotas, 2019 , De-La-Hoz-Correa et al, 2019 ).…”

Section: Resultsmentioning

confidence: 99%

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The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation

Chicco

Warrens

Jurman

2021

PeerJ Computer Science

1,848

574

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Regression analysis makes up a large part of supervised machine learning, and consists of the prediction of a continuous independent target from a set of other predictor variables. The difference between binary classification and regression is in the target range: in binary classification, the target can have only two values (usually encoded as 0 and 1), while in regression the target can have multiple values. Even if regression analysis has been employed in a huge number of machine learning studies, no consensus has been reached on a single, unified, standard metric to assess the results of the regression itself. Many studies employ the mean square error (MSE) and its rooted variant (RMSE), or the mean absolute error (MAE) and its percentage variant (MAPE). Although useful, these rates share a common drawback: since their values can range between zero and +infinity, a single value of them does not say much about the performance of the regression with respect to the distribution of the ground truth elements. In this study, we focus on two rates that actually generate a high score only if the majority of the elements of a ground truth group has been correctly predicted: the coefficient of determination (also known as R-squared or R2) and the symmetric mean absolute percentage error (SMAPE). After showing their mathematical properties, we report a comparison between R2 and SMAPE in several use cases and in two real medical scenarios. Our results demonstrate that the coefficient of determination (R-squared) is more informative and truthful than SMAPE, and does not have the interpretability limitations of MSE, RMSE, MAE and MAPE. We therefore suggest the usage of R-squared as standard metric to evaluate regression analyses in any scientific domain.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation

Chicco

Warrens

Jurman

2021

PeerJ Computer Science

1,848

574

View full text Add to dashboard Cite

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“…Features included in the data were chose based in literacy analysis such as [1], [2], [3], [4], [5], [6], [8], and there is a noticeable relationship between weight and height given by Equation (1).…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

“…Data source location Barranquilla – Colombia, Lima – Peru, City of Mexico - Mexico Data accessibility Data is within this article Related research article E. De-La-Hoz-Correa, F. Mendoza-Palechor, A. De-La-Hoz-Manotas, R. Morales-Ortega, B. Sánchez Hernández. Obesity Level Estimation Software based on Decision Trees, Journal of Computer Science, 67, 2019 [6] …”

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confidence: 99%

Dataset for estimation of obesity levels based on eating habits and physical condition in individuals from Colombia, Peru and Mexico

Palechor

Manotas

2019

Data in Brief

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This paper presents data for the estimation of obesity levels in individuals from the countries of Mexico, Peru and Colombia, based on their eating habits and physical condition. The data contains 17 attributes and 2111 records, the records are labeled with the class variable NObesity (Obesity Level), that allows classification of the data using the values of Insufficient Weight, Normal Weight, Overweight Level I, Overweight Level II, Obesity Type I, Obesity Type II and Obesity Type III. 77% of the data was generated synthetically using the Weka tool and the SMOTE filter, 23% of the data was collected directly from users through a web platform. This data can be used to generate intelligent computational tools to identify the obesity level of an individual and to build recommender systems that monitor obesity levels. For discussion and more information of the dataset creation, please refer to the full-length article “Obesity Level Estimation Software based on Decision Trees” (De-La-Hoz-Correa et al., 2019).

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“…This kind of validation was used because, in general, it is recommended for estimation accuracy (even if computational power enables the use of more folds) due to its relatively low bias and variance [62]. Moreover, the J48 algorithm was selected since it was proven in previous studies to have performed better than other algorithms [63][64][65][66].…”

Section: Methodsmentioning

confidence: 99%

PISIoT: A Machine Learning and IoT-Based Smart Health Platform for Overweight and Obesity Control

et al. 2019

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Overweight and obesity are affecting productivity and quality of life worldwide. The Internet of Things (IoT) makes it possible to interconnect, detect, identify, and process data between objects or services to fulfill a common objective. The main advantages of IoT in healthcare are the monitoring, analysis, diagnosis, and control of conditions such as overweight and obesity and the generation of recommendations to prevent them. However, the objects used in the IoT have limited resources, so it has become necessary to consider other alternatives to analyze the data generated from monitoring, analysis, diagnosis, control, and the generation of recommendations, such as machine learning. This work presents PISIoT: a machine learning and IoT-based smart health platform for the prevention, detection, treatment, and control of overweight and obesity, and other associated conditions or health problems. Weka API and the J48 machine learning algorithm were used to identify critical variables and classify patients, while Apache Mahout and RuleML were used to generate medical recommendations. Finally, to validate the PISIoT platform, we present a case study on the prevention of myocardial infarction in elderly patients with obesity by monitoring biomedical variables.

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Obesity Level Estimation Software based on Decision Trees

Abstract: This open access article is distributed under a Creative Commons Attribution (CC-BY) 3.0 license.

Cited by 33 publications

References 15 publications

The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation

The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation

Dataset for estimation of obesity levels based on eating habits and physical condition in individuals from Colombia, Peru and Mexico

PISIoT: A Machine Learning and IoT-Based Smart Health Platform for Overweight and Obesity Control

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