Rousset Sylvie, Angelo Aymeric, Hamadouche Toufik, Lacomme Philippe
University Clermont Auvergne, UNH, UMR1019, INRAE, 63000 Clermont Ferrand, France.
University Clermont Auvergne, LIMOS UMR CNRS 6158, 63000 Clermont Ferrand, France.
Healthcare (Basel). 2023 Apr 12;11(8):1101. doi: 10.3390/healthcare11081101.
The worldwide epidemic of weight gain and obesity is increasing in response to the evolution of lifestyles. Our aim is to provide a new predictive method for current and future weight status estimation based on individual and behavioral characteristics.
The data of 273 normal (NW), overweight (OW) and obese (OB) subjects were assigned either to the training or to the test sample. The multi-layer perceptron classifier (MLP) classified the data into one of the three weight statuses (NW, OW, OB), and the classification model accuracy was determined using the test dataset and the confusion matrix.
On the basis of age, height, light-intensity physical activity and the daily number of vegetable portions consumed, the multi-layer perceptron classifier achieved 75.8% accuracy with 90.3% for NW, 34.2% for OW and 66.7% for OB. The NW and OW subjects showed the highest and the lowest number of true positives, respectively. The OW subjects were very often confused with NW. The OB subjects were confused with OW or NW 16.6% of the time.
To increase the accuracy of the classification, a greater number of data and/or variables are needed.
随着生活方式的演变，全球范围内体重增加和肥胖的流行趋势日益加剧。我们的目标是基于个体和行为特征，提供一种用于当前和未来体重状况估计的新预测方法。
将273名正常体重（NW）、超重（OW）和肥胖（OB）受试者的数据分配到训练样本或测试样本中。多层感知器分类器（MLP）将数据分类为三种体重状况（NW、OW、OB）之一，并使用测试数据集和混淆矩阵确定分类模型的准确性。
基于年龄、身高、轻度体力活动和每日蔬菜摄入量，多层感知器分类器的准确率达到75.8%，其中正常体重者的准确率为90.3%，超重者为34.2%，肥胖者为66.7%。正常体重和超重受试者的真阳性数量分别最高和最低。超重受试者经常与正常体重者混淆。肥胖受试者有16.6%的时间被混淆为超重或正常体重。
为提高分类的准确性，需要更多的数据和/或变量。
