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Automatic segmentation to characterize anthropometric parameters and cardiovascular indicators in children

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Abstract A new predictive model to classify childhood obesity was implemented using machine learning techniques. The first step was to calculate the most relevant anthropomorphic and cardiovascular parameters of 187 children through principal component analysis (PCA) and cluster classification. Then Naïve‐Bayes method classified these children into six groups using anthropometric Z Score, measurements of abdominal obesity, and arterial pressure: Group I (20.32% of total): composed mainly by accentuated malnutrition and malnutrition children; Group II (36.36%): composed primarily by eutrophic children; Group III (21.4%): constituted by eutrophic plus overweight children; Group IV (14.97%): comprised mainly by overweight and obese children; Group V (5.34%): Obese and overweight children; and Group VI (1.6%): obese at risk children. From Group II to VI, the proportion of pre‐hypertensive and hypertensive children increased monotonically from 5 to 33%. This classification modes was tested on 66 children that were not originally included with a success rate of 97%. This predictive model will facilitate future longitudinal studies of obesity in children and will help plan interventions and evaluations of their results. This article is categorized under: Algorithmic Development > Biological Data Mining
CONSORT flow diagram
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Anthropometric and cardiovascular parameters visualization and classification of the patient based on the Naïve‐Bayes' method. (), patient ID#389; (), patient ID#368; 57 and 43% probability of belonging to Cluster II or III, respectively
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Performance of classifiers. Random Forest algorithm (Tin Kam, 1998), Naïve‐Bayes' method (Domingos & Pazzani, 1996). Decision tree method (J48) (Salzberg, 1994), conditional inference trees (Tree) (Hothorn, Hornik, & Zeileis, 2006), support vector machines (SVMs) with radial basis function, Kernel Cortes (Cortes & Vapnik, 1995; Scholkopf et al., 1997), Recursive Partitioning and Regression Trees (RPart) (Breiman, Friedman, Stone, & Olshen, 1984)
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Bidimensional clusters representation. The first principal component (PC1) represents 84.5% of the total variance and the second principal component (PC2) the 9.8%. The two dimensions represent approximately 94.3% of the sample
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(a) Biplot with waist‐to‐height ratio (WHtR), waist‐hip ratio (WHR), Z Score, body fat percentage (%Fat), and blood pressure (systolic blood pressure [SBP] and diastolic blood pressure [DBP]). (b) Biplot with the variables that contribute to the most variance of the total sample (Z Score, WHtR, %Fat). The vector module represents the importance of the variables on the sample
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Scheme of different steps and substeps of data processing. (1) Data processing, (2) dimensional reduction: principal component analysis (PCA) and clustering analysis, (3) modeling processing: Model selection, training, testing and implementation, and (4) prediction result through new data sample
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Algorithmic Development > Biological Data Mining

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