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Obesity is a serious public health problem because of risk factors for diseases and psychological problems. The focus of this study is to diagnose patient BMI (body mass index) status without weight and height measurement for use in future clinical applications. In this paper, we first propose a method for classifying a normal and overweight using only speech signals. Also, we perform a statistical analysis of the features from speech signals. Based on 1830 subjects, the accuracy and AUC (area under the ROC curve) of age- and gender-specific classifications ranged from 60.4 to 73.8% and from 0.628 to 0.738, respectively. We identified several features that were significantly different between normal and overweight subjects (p < 0.05). Also, we found compact and discriminatory feature subsets for building models for diagnosing normal or overweight individuals through wrapper-based feature subset selection. Our results showed that predicting BMI status is possible using a combination of speech features, even though significant features are rare and weak in age- and gender-specific groups, and that the classification accuracy with feature selection was higher than that without feature selection. Our method has the potential to be used in future clinical applications such as automatic BMI diagnosis in telemedicine or remote healthcare. |
