Regional foods in Brazilian school meals

Purpose – The purpose of this paper is to identify regional foods and analyze its use on school menus of a Brazilian city, as well as the respect to symbolic and cultural aspects related to it. Design/methodology/approach – The study was conducted in two stages. In the first stage, regional foods were identified through interviews with key school meal and city agents. In the second stage, the inclusion of these foods in school menus from 2009 to 2013 was assessed. Findings – In total, 142 regional foods were identified and classified into four groups. This classification resulted in a decision tree model to identify regional foods. Approximately 45 percent of regional preparations and 82.5 percent of regional foods were offered totaling 455 preparations and 977 foods analyzed. However, 31 percent of the regional foods identified in Stage 1 were not offered in the menus analyzed. Various regional preparations lost their authenticity, possibly not being recognized because of a lack of traditional ingredients or because they contained non-regional foods that changed their character. Practical implications – The results mainly point to symbolic aspects of the production and consumption of regional foods and preparations that are important to promoting healthy diets. In addition, they can support public policies that promote the use of these foods in the school environment. Originality/value – This study analyzes the inclusion of regional foods in school meals--a topic rarely explored in the scientific literature – and proposes a decision tree model to identify regional foods with methodological rigor. This model can assist school food managers in including regional foods and developing studies related to this topic.

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Personal bankruptcy prediction using decision tree model

Journal of Economics Finance and Administrative Science ◽

10.1108/jefas-08-2018-0076 ◽

2019 ◽

Vol 24 (47) ◽

pp. 157-170 ◽

Cited By ~ 1

Author(s):

Sharifah Heryati Syed Nor ◽

Shafinar Ismail ◽

Bee Wah Yap

Keyword(s):

Decision Tree ◽

Financial Institutions ◽

Negative Impact ◽

Bankruptcy Prediction ◽

Decision Tree Model ◽

Tree Model ◽

Personal Bankruptcy ◽

Content Type ◽

To Come ◽

The Right

Purpose Personal bankruptcy is on the rise in Malaysia. The Insolvency Department of Malaysia reported that personal bankruptcy has increased since 2007, and the total accumulated personal bankruptcy cases stood at 131,282 in 2014. This is indeed an alarming issue because the increasing number of personal bankruptcy cases will have a negative impact on the Malaysian economy, as well as on the society. From the aspect of individual’s personal economy, bankruptcy minimizes their chances of securing a job. Apart from that, their account will be frozen, lost control on their assets and properties and not allowed to start any business nor be a part of any company’s management. Bankrupts also will be denied from any loan application, restricted from travelling overseas and cannot act as a guarantor. This paper aims to investigate this problem by developing the personal bankruptcy prediction model using the decision tree technique. Design/methodology/approach In this paper, bankrupt is defined as terminated members who failed to settle their loans. The sample comprised of 24,546 cases with 17 per cent settled cases and 83 per cent terminated cases. The data included a dependent variable, i.e. bankruptcy status (Y = 1(bankrupt), Y = 0 (non-bankrupt)) and 12 predictors. SAS Enterprise Miner 14.1 software was used to develop the decision tree model. Findings Upon completion, this study succeeds to come out with the profiles of bankrupts, reliable personal bankruptcy scoring model and significant variables of personal bankruptcy. Practical implications This decision tree model is possible for patent and income generation. Financial institutions are able to use this model for potential borrowers to predict their tendency toward personal bankruptcy. Social implications Create awareness to society on significant variables of personal bankruptcy so that they can avoid being a bankrupt. Originality/value This decision tree model is able to facilitate and assist financial institutions in evaluating and assessing their potential borrower. It helps to identify potential defaulting borrowers. It also can assist financial institutions in implementing the right strategies to avoid defaulting borrowers.

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The decision tree for longer-stay hotel guest: the relationship between hotel booking determinants and geographical distance

International Journal of Contemporary Hospitality Management ◽

10.1108/ijchm-06-2020-0594 ◽

2020 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Yejin Lee ◽

Dae-Young Kim

Keyword(s):

Machine Learning ◽

Decision Tree ◽

Learning Algorithm ◽

Tourism Industry ◽

Decision Tree Model ◽

Geographical Distance ◽

Tree Model ◽

Content Type ◽

Hotel Booking ◽

The Relationship

Purpose Using the decision tree model, this study aims to understand the online travelers booking behaviors on Expedia.com, by examining influential determinants of online hotel booking, especially for longer-stay travelers. The geographical distance is also considered in understanding the booking behaviors trisecting travel destinations (i.e. Americas, Europe and Asia). Design/methodology/approach The data were obtained from American Statistical Association DataFest and Expedia.com. Based on the US travelers who made hotel reservation on the website, the study used a machine learning algorithm, decision tree, to analyze the influential determinants on hotel booking considering the geographical distance between origin and destination. Findings The results of the findings demonstrate that the choice of package product is the prioritized determinant for longer-stay hotel guests. Several similarities and differences were found from the significant determinants of the decision tree, in accordance with the geographic distance among the Americas, Europe and Asia. Research limitations/implications This paper presents the extension to an existing machine learning environment, and especially to the decision tree model. The findings are anticipated to expand the understanding of online hotel booking and apprehend the influential determinants toward consumers’ decision-making process regarding the relationship between geographical distance and traveler’s hotel staying duration. Originality/value This research brings a meaningful understanding of the hospitality and tourism industry, especially to the realm of machine learning adapted to an online booking website. It provides a unique approach to comprehend and forecast consumer behavior with data mining.

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Research on money laundering risk assessment of customers – based on the empirical research of China

Journal of Money Laundering Control ◽

10.1108/jmlc-01-2015-0004 ◽

2016 ◽

Vol 19 (3) ◽

pp. 249-263 ◽

Cited By ~ 5

Author(s):

Yao-Wen Xue ◽

Yan-Hua Zhang

Keyword(s):

Risk Assessment ◽

Decision Tree ◽

Money Laundering ◽

Decision Tree Model ◽

Tree Model ◽

Entropy Weight ◽

Content Type ◽

Regulatory Approach ◽

Entropy Weight Method ◽

Weight Method

Purpose To implement a risk-based regulatory approach, this paper aims to make an assessment on customers' money laundering risk and conducts some applications. Design/methodology/approach During the transition of a regulatory approach from “rule-based” to “risk-based”, this paper considers that the area of a customer, types of business and the industries to which the customer belongs are the main indicators to judge money laundering risk of a customer. Based on the statistical analysis of 221 typical money laundering cases, first-class index weights are given by using the entropy weight method and then by combining with the membership function, this paper determines a customer’s money laundering risk levels. On the basis of the entropy weight method, this paper uses the C5.0 algorithm to construct a decision tree model and then carries out application research on customer money laundering risk assessment to verify the effectiveness of the entropy weight method and the decision tree model. Findings This empirical research found the weights of three key money laundering indicators: customer areas, business types and corresponding industries. Originality/value Asserting that current money laundering risk assessments of customers are marginal at best, this paper contends from the perspective of practice, and applies the entropy weight method and the decision tree model for money laundering risk assessment of customers.

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Prediction of Antimicrobial Activity of Synthetic Peptides by a Decision Tree Model

Applied and Environmental Microbiology ◽

10.1128/aem.02804-12 ◽

2013 ◽

Vol 79 (10) ◽

pp. 3156-3159 ◽

Cited By ~ 17

Author(s):

Felipe Lira ◽

Pedro S. Perez ◽

José A. Baranauskas ◽

Sérgio R. Nozawa

Keyword(s):

Antimicrobial Activity ◽

Decision Tree ◽

Antimicrobial Peptides ◽

Synthetic Peptides ◽

Decision Tree Model ◽

Tree Model ◽

Computational Tools ◽

Content Type ◽

Persistent Problem ◽

Peptide Database

ABSTRACTAntimicrobial resistance is a persistent problem in the public health sphere. However, recent attempts to find effective substitutes to combat infections have been directed at identifying natural antimicrobial peptides in order to circumvent resistance to commercial antibiotics. This study describes the development of synthetic peptides with antimicrobial activity, createdin silicoby site-directed mutation modeling using wild-type peptides as scaffolds for these mutations. Fragments of antimicrobial peptides were used for modeling with molecular modeling computational tools. To analyze these peptides, a decision tree model, which indicated the action range of peptides on the types of microorganisms on which they can exercise biological activity, was created. The decision tree model was processed using physicochemistry properties from known antimicrobial peptides available at the Antimicrobial Peptide Database (APD). The two most promising peptides were synthesized, and antimicrobial assays showed inhibitory activity against Gram-positive and Gram-negative bacteria. Colossomin C and colossomin D were the most inhibitory peptides at 5 μg/ml againstStaphylococcus aureusandEscherichia coli. The methods described in this work and the results obtained are useful for the identification and development of new compounds with antimicrobial activity through the use of computational tools.

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A novel enhanced decision tree model for detecting chronic kidney disease

Network Modeling Analysis in Health Informatics and Bioinformatics ◽

10.1007/s13721-021-00302-w ◽

2021 ◽

Vol 10 (1) ◽

Author(s):

Avijit Kumar Chaudhuri ◽

Deepankar Sinha ◽

Dilip K. Banerjee ◽

Anirban Das

Keyword(s):

Chronic Kidney Disease ◽

Kidney Disease ◽

Decision Tree ◽

Decision Tree Model ◽

Tree Model

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Risk of Pre-Malignancy or Malignancy in Postmenopausal Endometrial Polyps: A CHAID Decision Tree Analysis

Diagnostics ◽

10.3390/diagnostics11061094 ◽

2021 ◽

Vol 11 (6) ◽

pp. 1094

Author(s):

Michael Wong ◽

Nikolaos Thanatsis ◽

Federica Nardelli ◽

Tejal Amin ◽

Davor Jurkovic

Keyword(s):

Decision Tree ◽

Expectant Management ◽

Decision Tree Model ◽

Decision Tree Analysis ◽

Focal Lesions ◽

Tree Model ◽

Normal Endometrium ◽

Endometrial Polyps ◽

Tree Analysis ◽

Interaction Detection

Background and aims: Postmenopausal endometrial polyps are commonly managed by surgical resection; however, expectant management may be considered for some women due to the presence of medical co-morbidities, failed hysteroscopies or patient’s preference. This study aimed to identify patient characteristics and ultrasound morphological features of polyps that could aid in the prediction of underlying pre-malignancy or malignancy in postmenopausal polyps. Methods: Women with consecutive postmenopausal polyps diagnosed on ultrasound and removed surgically were recruited between October 2015 to October 2018 prospectively. Polyps were defined on ultrasound as focal lesions with a regular outline, surrounded by normal endometrium. On Doppler examination, there was either a single feeder vessel or no detectable vascularity. Polyps were classified histologically as benign (including hyperplasia without atypia), pre-malignant (atypical hyperplasia), or malignant. A Chi-squared automatic interaction detection (CHAID) decision tree analysis was performed with a range of demographic, clinical, and ultrasound variables as independent, and the presence of pre-malignancy or malignancy in polyps as dependent variables. A 10-fold cross-validation method was used to estimate the model’s misclassification risk. Results: There were 240 women included, 181 of whom presented with postmenopausal bleeding. Their median age was 60 (range of 45–94); 18/240 (7.5%) women were diagnosed with pre-malignant or malignant polyps. In our decision tree model, the polyp mean diameter (≤13 mm or >13 mm) on ultrasound was the most important predictor of pre-malignancy or malignancy. If the tree was allowed to grow, the patient’s body mass index (BMI) and cystic/solid appearance of the polyp classified women further into low-risk (≤5%), intermediate-risk (>5%–≤20%), or high-risk (>20%) groups. Conclusions: Our decision tree model may serve as a guide to counsel women on the benefits and risks of surgery for postmenopausal endometrial polyps. It may also assist clinicians in prioritizing women for surgery according to their risk of malignancy.

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Reanalysis and External Validation of a Decision Tree Model for Detecting Unrecognized Diabetes in Rural Chinese Individuals

International Journal of Endocrinology ◽

10.1155/2017/3894870 ◽

2017 ◽

Vol 2017 ◽

pp. 1-6 ◽

Cited By ~ 2

Author(s):

Zhong Xin ◽

Lin Hua ◽

Xu-Hong Wang ◽

Dong Zhao ◽

Cai-Guo Yu ◽

...

Keyword(s):

Decision Tree ◽

Predictive Value ◽

Early Stage ◽

Current Model ◽

External Validation ◽

Area Under The Curve ◽

Decision Tree Model ◽

Tree Model ◽

Chinese Adult ◽

Significant Difference

We reanalyzed previous data to develop a more simplified decision tree model as a screening tool for unrecognized diabetes, using basic information in Beijing community health records. Then, the model was validated in another rural town. Only three non-laboratory-based risk factors (age, BMI, and presence of hypertension) with fewer branches were used in the new model. The sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve (AUC) for detecting diabetes were calculated. The AUC values in internal and external validation groups were 0.708 and 0.629, respectively. Subjects with high risk of diabetes had significantly higher HOMA-IR, but no significant difference in HOMA-B was observed. This simple tool will help general practitioners and residents assess the risk of diabetes quickly and easily. This study also validates the strong associations of insulin resistance and early stage of diabetes, suggesting that more attention should be paid to the current model in rural Chinese adult populations.

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