id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_6mlcjuckcve7pcpruferfakk64	Alba Arceo-Vilas	Identification of predictive factors of the degree of adherence to the Mediterranean diet through machine-learning techniques	2020	21	.pdf	application/pdf	10833	1205	59	Keywords Feature selection, Nutritional status, Machine learning, Mediterranean diet, the degree of adherence to the Mediterranean diet through machine-learning techniques. Thus, this study is relevant for understanding how to measure the degree of adherence, The information described below was collected from each selected subject: sociodemographic variables: age, gender, level of education, marital status and relationships of Once the data of the different measurements were obtained, the mid-arm muscle best model in order to ensure whether the performance of a particular ML technique is Figure 2 Summary of the average performance of the experiments: (A) (Accuracy) and (B) (F-measure) of the four ML techniques (RF, SVM, predictive factors of the degree of adherence to the Mediterranean diet through Adherence to Mediterranean diet and bone health. Identification of predictive factors of the degree of adherence to the Mediterranean diet through machine-learning techniques Identification of predictive factors of the degree of adherence to the Mediterranean diet through machine-learning techniques	./cache/work_6mlcjuckcve7pcpruferfakk64.pdf	./txt/work_6mlcjuckcve7pcpruferfakk64.txt