scholarly journals Classification tree analysis for an intersectionality-informed identification of population groups with non-daily vegetable intake

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Emily Mena ◽  
Gabriele Bolte ◽  
Christine Holmberg ◽  
Philipp Jaehn ◽  
Sibille Merz ◽  
...  
Keyword(s):  
Vegetable Intake ◽  
Communicable Disease ◽  
Classification Tree ◽  
Regression Tree ◽  
Educational Status ◽  
Conditional Inference ◽  
Classification And Regression Tree ◽  
Blue Collar ◽  
Cross Sectional ◽  
Cart Algorithm

Abstract Background Daily vegetable intake is considered an important behavioural health resource associated with improved immune function and lower incidence of non-communicable disease. Analyses of population-based data show that being female and having a high educational status is most strongly associated with increased vegetable intake. In contrast, men and individuals with a low educational status seem to be most affected by non-daily vegetable intake (non-DVI). From an intersectionality perspective, health inequalities are seen as a consequence of an unequal balance of power such as persisting gender inequality. Unravelling intersections of socially driven aspects underlying inequalities might be achieved by not relying exclusively on the male/female binary, but by considering different facets of gender roles as well. This study aims to analyse possible interactions of sex/gender or sex/gender related aspects with a variety of different socio-cultural, socio-demographic and socio-economic variables with regard to non-DVI as the health-related outcome. Method Comparative classification tree analyses with classification and regression tree (CART) and conditional inference tree (CIT) as quantitative, non-parametric, exploratory methods for the detection of subgroups with high prevalence of non-DVI were performed. Complete-case analyses (n = 19,512) were based on cross-sectional data from a National Health Telephone Interview Survey conducted in Germany. Results The CART-algorithm constructed overall smaller trees when compared to CIT, but the subgroups detected by CART were also detected by CIT. The most strongly differentiating factor for non-DVI, when not considering any further sex/gender related aspects, was the male/female binary with a non-DVI prevalence of 61.7% in men and 42.7% in women. However, the inclusion of further sex/gender related aspects revealed a more heterogenous distribution of non-DVI across the sample, bringing gendered differences in main earner status and being a blue-collar worker to the foreground. In blue-collar workers who do not live with a partner on whom they can rely on financially, the non-DVI prevalence was 69.6% in men and 57.4% in women respectively. Conclusions Public health monitoring and reporting with an intersectionality-informed and gender-equitable perspective might benefit from an integration of further sex/gender related aspects into quantitative analyses in order to detect population subgroups most affected by non-DVI.

scholarly journals Implications of Lifestyle and Occupational Factors on the Risk of Breast Cancer in Shiftwork Nurses

Healthcare ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 649
Author(s):  
Javier Fagundo-Rivera ◽  
Regina Allande-Cussó ◽  
Mónica Ortega-Moreno ◽  
Juan Jesús García-Iglesias ◽  
Adolfo Romero ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Shift Work ◽  
Regression Tree ◽  
Night Work ◽  
Circadian Disruption ◽  
Classification And Regression Tree ◽  
Baseline Risk ◽  
Unhealthy Lifestyle ◽  
Cross Sectional ◽  
Occupational Factors

Shift work that involves circadian disruption has been highlighted as a likely carcinogenic factor for breast cancer in humans. Also, unhealthy lifestyle habits observed in night work nurses could be causally related to an increase in the incidence of estrogen-positive breast tumours in this population. Assessing baseline risk of breast cancer in nurses is essential. The objective of this study was to analyze the risk of breast cancer that nurses had in relation to their lifestyle and labour factors related to shift work. A cross-sectional descriptive study through a questionnaire about sociodemographic variables, self-perception of health, and working life was designed. The sample consisted of 966 nurses. The relationship between variables was tested. A binary logistic regression and a classification and regression tree were performed. The most significant labour variables in relation to the risk of breast cancer were the number of years worked (more than 16 years; p < 0.01; OR = 8.733, 95% CI = 2.811, 27.134) and the total years performing more than 3 nights per month (10 or more years; p < 0.05; OR = 2.294, 95% CI = 1.008, 5.220). Also, the nights worked throughout life (over 500; OR = 4.190, 95% CI = 2.118, 8.287) were significant in the analysis. Nurses who had or ever had breast cancer valued their self-perceived health more negatively (p < 0.001) and referred a lower quality of sleep (p < 0.001) than the non-cases nurses. The occupational factors derived from night work could have several impacts on nurses’ health and their family-work balance. Promoting healthy lifestyles, informing about shift work risks, and adjusting shift work schedules are critical methods to decrease the possible effects of circadian disruption in nurses.

scholarly journals A Novel Comprehensive Clinical Stratification Model to Refine Prognosis of Glioblastoma Patients Undergoing Surgical Resection

Cancers ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 386 ◽  
Author(s):  
Tamara Ius ◽  
Fabrizio Pignotti ◽  
Giuseppe Maria Della Pepa ◽  
Giuseppe La Rocca ◽  
Teresa Somma ◽  
...  
Keyword(s):  
Surgical Resection ◽  
Classification Tree ◽  
Prognostic Score ◽  
Regression Tree ◽  
Image Data ◽  
Extent Of Resection ◽  
Classification And Regression Tree ◽  
Postoperative Treatment ◽  
Time Data ◽  
Cart Analysis

Despite recent discoveries in genetics and molecular fields, glioblastoma (GBM) prognosis still remains unfavorable with less than 10% of patients alive 5 years after diagnosis. Numerous studies have focused on the research of biological biomarkers to stratify GBM patients. We addressed this issue in our study by using clinical/molecular and image data, which is generally available to Neurosurgical Departments in order to create a prognostic score that can be useful to stratify GBM patients undergoing surgical resection. By using the random forest approach [CART analysis (classification and regression tree)] on Survival time data of 465 cases, we developed a new prediction score resulting in 10 groups based on extent of resection (EOR), age, tumor volumetric features, intraoperative protocols and tumor molecular classes. The resulting tree was trimmed according to similarities in the relative hazard ratios amongst groups, giving rise to a 5-group classification tree. These 5 groups were different in terms of overall survival (OS) (p < 0.000). The score performance in predicting death was defined by a Harrell’s c-index of 0.79 (95% confidence interval [0.76–0.81]). The proposed score could be useful in a clinical setting to refine the prognosis of GBM patients after surgery and prior to postoperative treatment.

Prospection of immunological biomarkers for characterization and monitoring of asymptomatic Leishmania (Leishmania) infantum infection

Parasitology ◽  
2020 ◽  
Vol 147 (10) ◽  
pp. 1124-1132
Author(s):  
Gisele Macêdo Rodrigues da Cunha ◽  
Mariângela Carneiro ◽  
Marcelo Antônio Pascoal-Xavier ◽  
Iara Caixeta Marques da Rocha ◽  
Fernanda do Carmo Magalhães ◽  
...  
Keyword(s):  
Leishmania Infantum ◽  
Regression Tree ◽  
Cross Sectional Study ◽  
Asymptomatic Infection ◽  
Classification And Regression Tree ◽  
Soluble Antigen ◽  
Serum Samples ◽  
Serological Tests ◽  
Asymptomatic Group ◽  
Cross Sectional

AbstractIn areas endemic for Leishmania infantum, an asymptomatic infection may be an indicator of the extent of transmission. The main goal of this study was to evaluate the applicability of measuring circulating immunological biomarkers as an alternative strategy to characterize and monitor L. infantum asymptomatic infections in combination with serological methods. To this end, 179 children from a region endemic for visceral leishmaniasis (VL), aged 1–10 years old, selected from a cross-sectional study, were identified as asymptomatic (n = 81) or uninfected (n = 98) by qPCR and/or serological tests (ELISA using L. infantum soluble antigen and rK39), and, together with serum samples of children diagnosed with VL (n = 43), were subjected to avidity tests and cytokine levels measurement. Avidity rates (AR) ranging from 41 to 70% were found in 29 children (66%) from the asymptomatic group. On the other hand, high AR (above 70%) were observed in 27 children (64%) from the VL group. Logistic Regression and Classification and Regression Tree (CART) analyses demonstrated that lower AR and IFN-γ production associated with higher IL-17A levels were hallmarks in asymptomatic L. infantum infections. Therefore, this study proposes an association of immunological biomarkers that can be used as a complementary strategy for the characterization and monitoring of asymptomatic VL infections in children living in endemic areas.

scholarly journals Are normoglycaemic individuals at risk of depression? The depression-dysglycaemic phenotype from a European population-based cross-sectional study

2019 ◽  
Author(s):  
Sarah Cuschieri ◽  
Julian Mamo
Keyword(s):  
Communicable Disease ◽  
Population Level ◽  
Female Gender ◽  
Educational Status ◽  
Population Based ◽  
Cross Sectional Study ◽  
Health Concern ◽  
Sectional Study ◽  
Cross Sectional ◽  
History Of

Abstract Background Depression is an ever more common chronic non communicable disease and its control constitutes a growing public health concern given its links with a number of co-morbidities, including diabetes mellitus. The study aimed to estimate the prevalence of depression at a population level across groups of different glycaemic status, whilst establishing its socioeconomic phenotypic characteristics.Methods A nationally representative cross-sectional study was conducted in Malta between 2014 and 2016. Participants were categorized into different sub-populations according to their glycaemic status. Depression prevalence rates and socio-economic characteristics for each sub-population were established. Multiple regression analysis was performed to identify links with depression.Results Depression was prevalent in 17.15% (CI 95%: 16.01 – 18.36) with a female predominance. The normoglycaemic sub-population had the highest depression rates. However, persons with known diabetes had a higher probability of having a history of depression (OR:2.36 CI 95%:1.12 – 4.96), as well as with being of the female gender, having lower educational status, having a history of smoking tobacco and having established cardiovascular disease.Conclusions Depression was highly prevalent among the normoglycaemic population especially as age progress. Physicians in primary care should implement a depression screening tool as part of their routine health check-ups, with special attention to those with cardiovascular co-morbidities and any signs of psycho-socio-economic burden.

Using machine learning techniques to develop prediction models for detecting unpaid credit card customers

2020 ◽  
Vol 39 (5) ◽  
pp. 6073-6087
Author(s):  
Meltem Yontar ◽  
Özge Hüsniye Namli ◽  
Seda Yanik
Keyword(s):  
Decision Tree ◽  
Credit Card ◽  
Banking Sector ◽  
Performance Metrics ◽  
Prediction Models ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Cart Algorithm

Customer behavior prediction is gaining more importance in the banking sector like in any other sector recently. This study aims to propose a model to predict whether credit card users will pay their debts or not. Using the proposed model, potential unpaid risks can be predicted and necessary actions can be taken in time. For the prediction of customers’ payment status of next months, we use Artificial Neural Network (ANN), Support Vector Machine (SVM), Classification and Regression Tree (CART) and C4.5, which are widely used artificial intelligence and decision tree algorithms. Our dataset includes 10713 customer’s records obtained from a well-known bank in Taiwan. These records consist of customer information such as the amount of credit, gender, education level, marital status, age, past payment records, invoice amount and amount of credit card payments. We apply cross validation and hold-out methods to divide our dataset into two parts as training and test sets. Then we evaluate the algorithms with the proposed performance metrics. We also optimize the parameters of the algorithms to improve the performance of prediction. The results show that the model built with the CART algorithm, one of the decision tree algorithm, provides high accuracy (about 86%) to predict the customers’ payment status for next month. When the algorithm parameters are optimized, classification accuracy and performance are increased.

scholarly journals Statistical Analysis of Treatment Planning Parameters for Prediction of Delivery Quality Assurance Failure for Helical Tomotherapy

2020 ◽  
Vol 19 ◽  
pp. 153303382097969
Author(s):  
Kyung Hwan Chang ◽  
Young Hyun Lee ◽  
Byung Hun Park ◽  
Min Cheol Han ◽  
Jihun Kim ◽  
...  
Keyword(s):  
Quality Assurance ◽  
Treatment Planning ◽  
Planning Process ◽  
Helical Tomotherapy ◽  
Regression Tree ◽  
Roc Curves ◽  
Classification And Regression Tree ◽  
Variable Model ◽  
Predicted Probability ◽  
Cart Algorithm

Purpose: This study aimed to investigate the parameters with a significant impact on delivery quality assurance (DQA) failure and analyze the planning parameters as possible predictors of DQA failure for helical tomotherapy. Methods: In total, 212 patients who passed or failed DQA measurements were retrospectively included in this study. Brain (n = 43), head and neck (n = 37), spinal (n = 12), prostate (n = 36), rectal (n = 36), pelvis (n = 13), cranial spinal irradiation and a treatment field including lymph nodes (n = 24), and other types of cancer (n = 11) were selected. The correlation between DQA results and treatment planning parameters were analyzed using logistic regression analysis. Receiver operating characteristic (ROC) curves, areas under the curves (AUCs), and the Classification and Regression Tree (CART) algorithm were used to analyze treatment planning parameters as possible predictors for DQA failure. Results: The AUC for leaf open time (LOT) was 0.70, and its cut-off point was approximately 30%. The ROC curve for the predicted probability calculated when the multivariate variable model was applied showed an AUC of 0.815. We confirmed that total monitor units, total dose, and LOT were significant predictors for DQA failure using the CART. Conclusions: The probability of DQA failure was higher when the percentage of LOT below 100 ms was higher than 30%. The percentage of LOT below 100 ms should be considered in the treatment planning process. The findings from this study may assist in the prediction of DQA failure in the future.

Remote Sense Image Classification Based on CART Algorithm

2013 ◽  
Vol 864-867 ◽  
pp. 2782-2786
Author(s):  
Bao Hua Yang ◽  
Shuang Li
Keyword(s):  
Remote Sensing ◽  
Decision Tree ◽  
Regression Tree ◽  
Remote Sensing Image ◽  
Classification And Regression Tree ◽  
Classification Method ◽  
Remote Sense ◽  
Data Source ◽  
Cart Algorithm ◽  
Tree Method

This papers deals with the study of the algorithm of classification method based on decision tree for remote sensing image. The experimental area is located in the Xiangyang district, the data source for the 2010 satellite images of SPOT and TM fusion. Moreover, classification method based on decision tree is optimized with the help of the module of RuleGen and applied in regional remote sensing image of interest. The precision of Maximum likelihood ratio is 95.15 percent, and 94.82 percent for CRAT. Experimental results show that the classification method based on classification and regression tree method is as well as the traditional one.

scholarly journals IDENTIFYING THE KEY RISK FACTORS OF TRAFFIC ACCIDENT INJURY SEVERITY ON SLOVENIAN ROADS USING A NON-PARAMETRIC CLASSIFICATION TREE

Transport ◽  
2014 ◽  
Vol 32 (3) ◽  
pp. 272-281 ◽  
Author(s):  
Vesna Rovšek ◽  
Milan Batista ◽  
Branko Bogunović
Keyword(s):  
Human Error ◽  
Traffic Accidents ◽  
Injury Severity ◽  
Classification Tree ◽  
Classification And Regression Tree ◽  
Importance Measure ◽  
Economic Point ◽  
Parametric Classification ◽  
Cart Algorithm ◽  
Non Parametric

From both a practical and economic point of view, road transport meets almost all the requirements of modern life, but it is also a source of numerous negative effects, including traffic accidents. In order to design a safe transport system and achieve the ‘zero vision’ goal – no serious injuries or fatalities in traffic accidents – there is a growing need for a systematic approach to this problem. Prior to the assessment of any accident prevention measure it is necessary to identify the most important factors and significant patterns which affect the severity of accidents and injuries. In this study, the crash data from Slovenia pertaining to the period 2005–2009 were analysed with a Classification and Regression Tree (CART) algorithm, one of the most widely applied data mining technique when analysing a large amount of data with several independent quantitative or qualitative variables. Before building a non-parametric classification tree, the data were split into three totally separate subsets, the training set, the testing set, and the evaluation set. Moreover, using the Variable Importance Measure (VIM) the factor of influence of nine independent variables on the target variables were calculated. The results confirm that traffic accidents and injuries on Slovenian roads are caused by a combination of factors, the most important of them being human error, or more precisely, speeding and driving in the wrong lane.

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