Classification and features selection method for obesity level prediction
Artículo de revista
2021-06-15
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.
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