Health Star Rating of Nonalcoholic, Packaged, and Ready-to-Drink Beverages in Türkiye: A Decision Tree Model Study.

Prev Nutr Food Sci

Department of Nutrition and Dietetics, Faculty of Health Sciences, Kırıkkale University, Kırıkkale 71450, Türkiye.

Published: June 2024

This study aimed to compare the nutritional quality of beverages sold in Türkiye according to their labeling profiles. A total of 304 nonalcoholic beverages sold in supermarkets and online markets with the highest market capacity in Türkiye were included. Milk and dairy products, sports drinks, and beverages for children were excluded. The health star rating (HSR) was used to assess the nutritional quality of beverages. The nutritional quality of beverages was evaluated using a decision tree model according to the HSR score based on the variables presented on the beverage label. Moreover, confusion matrix tests were used to test the model's accuracy. The mean HSR score of beverages was 2.6±1.9, of which 30.2% were in the healthy category (HSR≥3.5). Fermented and 100% fruit juice beverages had the highest mean HSR scores. According to the decision tree model of the training set, the predictors of HSR quality score, in order of importance, were as follows: added sugar (46%), sweetener (28%), additives (19%), fructose-glucose syrup (4%), and caffeine (3%). In the test set, the accuracy rate and F1 score were 0.90 and 0.82, respectively, suggesting that the prediction performance of our model had the perfect fit. According to the HSR classification, most beverages were found to be unhealthy. Thus, they increase the risk of the development of obesity and other diseases because of their easy consumption. The decision tree learning algorithm could guide the population to choose healthy beverages based on their labeling information.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11223921PMC
http://dx.doi.org/10.3746/pnf.2024.29.2.199DOI Listing

Publication Analysis

Top Keywords

decision tree
16
tree model
12
nutritional quality
12
quality beverages
12
beverages
10
health star
8
star rating
8
beverages sold
8
hsr score
8
hsr
6

Similar Publications

Machine learning techniques for non-destructive estimation of plum fruit weight.

Sci Rep

January 2025

Crop and Horticultural Science Research Department, Mazandaran Agricultural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Tajrish, Iran.

Plum fruit fresh weight (FW) estimation is crucial for various agricultural practices, including yield prediction, quality control, and market pricing. Traditional methods for estimating fruit weight are often destructive, time-consuming, and labor-intensive. In this study, we addressed the problem of predicting plum FW using artificial intelligence (AI) methods based on fruit dimensions.

View Article and Find Full Text PDF

Machine learning to predict the decision to perform surgery in hepatic echinococcosis.

HPB (Oxford)

December 2024

Fondazione IRCCS Policlinico San Matteo, SC Chirurgia Generale 1, Pavia, Italy. Electronic address:

Background: Cystic echinococcosis (CE) is a significant public health issue, primarily affecting the liver. While several management strategies exist, there is a lack of predictive tools to guide surgical decisions for hepatic CE. This study aimed to develop predictive models to support surgical decision-making in hepatic CE, enhancing the precision of patient allocation to surgical or non-surgical management pathways.

View Article and Find Full Text PDF

Background: Gestational Diabetes Mellitus (GDM) is a common complication during pregnancy. Late diagnosis can have significant implications for both the mother and the fetus. This research aims to create an early prediction model for GDM in the first trimester of pregnancy.

View Article and Find Full Text PDF

The two sides of Phobos: Gray and white matter abnormalities in phobic individuals.

Cogn Affect Behav Neurosci

January 2025

Departamento de Psicología ClínicaPsicobiología y MetodologíaFacultad de Psicología, Universidad de La Laguna, La Laguna, 38200, Tenerife, Spain.

Small animal phobia (SAP) is a subtype of specific phobia characterized by an intense and irrational fear of small animals, which has been underexplored in the neuroscientific literature. Previous studies often faced limitations, such as small sample sizes, focusing on only one neuroimaging modality, and reliance on univariate analyses, which produced inconsistent findings. This study was designed to overcome these issues by using for the first time advanced multivariate machine-learning techniques to identify the neural mechanisms underlying SAP.

View Article and Find Full Text PDF

Habitat radiomics based on CT images to predict survival and immune status in hepatocellular carcinoma, a multi-cohort validation study.

Transl Oncol

January 2025

Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin 300060, China; Tianjin's Clinical Research Center for Cancer, Tianjin 300060, China. Electronic address:

Background And Objective: Though several clinicopathological features are identified as prognostic indicators, potentially prognostic radiomic models are expected to preoperatively and noninvasively predict survival for HCC. Traditional radiomic models are lacking in a consideration for intratumoral regional heterogeneity. The study aimed to establish and validate the predictive power of multiple habitat radiomic models in predicting prognosis of hepatocellular carcinoma (HCC).

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!