Missing Posterior Teeth and Risk of Temporomandibular Disorders

2009 ◽  
Vol 88 (10) ◽  
pp. 942-945 ◽  
Author(s):  
M.Q. Wang ◽  
F. Xue ◽  
J.J. He ◽  
J.H. Chen ◽  
C.S. Chen ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Young Women ◽  
Temporomandibular Disorders ◽  
Logistic Model ◽  
Logistic Regression Model ◽  
Age And Gender ◽  
Posterior Teeth ◽  
Men 1 ◽  
And Gender

There is disagreement about the association between missing posterior teeth and the presence of temporomandibular disorders (TMD). Here, the purpose was to investigate whether the number of missing posterior teeth, their distribution, age, and gender are associated with TMD. Seven hundred and forty-one individuals, aged 21–60 years, with missing posterior teeth, 386 with and 355 without TMD, were included. Four variables—gender, age, the number of missing posterior teeth, and the number of dental quadrants with missing posterior teeth—were analyzed with a logistic regression model. All four variables—gender (OR = 1.59, men = 1, women = 2), age (OR = 0.98), the number of missing posterior teeth (OR = 0.51), and the number of dental quadrants with missing posterior teeth (OR = 7.71)—were entered into the logistic model (P < 0.01). The results indicate that individuals who lose posterior teeth, with fewer missing posterior teeth but in more quadrants, have a higher prevalence of TMD, especially young women.

scholarly journals Sexual dimorphism of viscerocranium-A logistic model

2021 ◽  
Vol 6 (2) ◽  
Author(s):  
Lucas del Vigna Peixoto ◽  
Stefany de Lima Gomes ◽  
Ana Amelia Barbieri ◽  
Francisco Carlos Groppo ◽  
Cristhiane Martins Schmidt ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Sexual Dimorphism ◽  
Regression Model ◽  
Logistic Model ◽  
Quantitative Method ◽  
Logistic Regression Model ◽  
Sexually Dimorphic ◽  
Qualitative And Quantitative ◽  
The Right

Introduction: Sex estimates are generally based on the evaluation of qualitative and quantitative aspects of anatomic structures, however, the latter has better reproducibility and reliability. Objective: Aiming to evaluate the viscerocranium as a tool for sexual prediction and verify the possibility of creation of a logistic regression model for sexual prediction. Materials and Methods: 167 craniums - 100 male and 67 female between 22 and 85 years old from a Brazilian university´s Biobank - were evaluated. Results: It was observed that of the measures carried out were presented as sexually dimorphic, except for the measures of the right frontozygomatic point – right zygion; left frontozygomatic point – left zygion. Besides, it was possible to create a logistic regression model Sex = [logits/Sex = -24.5 + (0.20 * Nasion - Naso spine) + (0.18 * Right zygion - Naso spine)]. Conclusion: It was concluded that the measures of the viscerocranium present themselves as a factor of sexual dimorphism and the quantitative method developed was 81.4% accurate.

scholarly journals Early Detection of Severe Functional Impairment Among Adolescents With Major Depression Using Logistic Classifier

2021 ◽  
Vol 8 ◽  
Author(s):  
I.-Ming Chiu ◽  
Wenhua Lu ◽  
Fangming Tian ◽  
Daniel Hart
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Model ◽  
Logistic Regression Model ◽  
Age Groups ◽  
Recall Rate ◽  
Training Data ◽  
Statistical Tool ◽  
Data Set ◽  
Severe Impairment

Machine learning is about finding patterns and making predictions from raw data. In this study, we aimed to achieve two goals by utilizing the modern logistic regression model as a statistical tool and classifier. First, we analyzed the associations between Major Depressive Episode with Severe Impairment (MDESI) in adolescents with a list of broadly defined sociodemographic characteristics. Using findings from the logistic model, the second and ultimate goal was to identify the potential MDESI cases using a logistic model as a classifier (i.e., a predictive mechanism). Data on adolescents aged 12–17 years who participated in the National Survey on Drug Use and Health (NSDUH), 2011–2017, were pooled and analyzed. The logistic regression model revealed that compared with males and adolescents aged 12-13, females and those in the age groups of 14-15 and 16-17 had higher risk of MDESI. Blacks and Asians had lower risk of MDESI than Whites. Living in single-parent household, having less authoritative parents, having negative school experiences further increased adolescents' risk of having MDESI. The predictive model successfully identified 66% of the MDESI cases (recall rate) and accurately identified 72% of the MDESI and MDESI-free cases (accuracy rate) in the training data set. The rates of both recall and accuracy remained about the same (66 and 72%) using the test data. Results from this study confirmed that the logistic model, when used as a classifier, can identify potential cases of MDESI in adolescents with acceptable recall and reasonable accuracy rates. The algorithmic identification of adolescents at risk for depression may improve prevention and intervention.

Study on Early Warning of Enterprise Financial Distress — Based on Partial Least-squares Logistic Regression

2015 ◽  
Vol 65 (s2) ◽  
pp. 3-16 ◽  
Author(s):  
Kun Xu ◽  
Qilan Zhao ◽  
Xinzhong Bao
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Least Squares ◽  
Partial Least Squares ◽  
Early Warning ◽  
Financial Distress ◽  
Logistic Model ◽  
Logistic Regression Model ◽  
Warning System ◽  
Study Results

Establishment of an effective early warning system can make the company operators make relevant decisions as soon as possible when finding the crisis, improve the operating results and financial condition of enterprise, and can also make investors avoid or reduce investment losses. This paper applies the partial least-squares logistic regression model for the analysis on early warning of enterprise financial distress in consideration of quite sensitive characteristics of common logistic model for the multicollinearity. The data of real estate industry listed companies in China are used to compare and analyze the early warning of financial distress by using the logistic model and the partial least-squares logistic model, respectively. The study results show that compared with the common logistic regression model, the applicability of partial least-squares logistic model is stronger due to its eliminating multicollinearity problem among various early warning indicators.

scholarly journals Evaluation of probability of linkages between enterprises and farmer's potatoes in Lam Dong province

2019 ◽  
Vol 18 (01) ◽  
pp. 1-8
Author(s):  
Nam H. Tran
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Multinomial Logistic Regression ◽  
Potato Production ◽  
Farm Size ◽  
Multinomial Logistic Regression Model ◽  
Factors Affecting ◽  
And Gender

In this study, the collaboration between buyer and the famers in potato production was evaluated by using a multinomial Logistic regression model with MLE estimating. The data were collected by directly interviewing of 245 farmers at the Xuan Tho commune, Da Lat city and Don Duong district, Lam Dong province. Results of the research showed that a tight collaboration between the companies and farmers would increase in a higher productivity. The estimation showed that the probability of farmer which would and would not collaborate with buyer were 14.6% (Y2/Y1) and 63.0% (Y3/Y1). The factors affecting the probability of linkages between enterprises and farmers were enterprises and farmers were experience, farm, size, profit, policy supports and gender. The results also revealed that when price of potatoes increase, farmers would not comply with agreement.

scholarly journals Analysis of Influencing Factors of Community Management of Diabetes based on Adaptive-Lasso Logistic Regression Model

2020 ◽  
Author(s):  
Wei Lin ◽  
Zejuan Huang ◽  
Zhiqing Cao ◽  
Jing Zhou ◽  
Yang Tian ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Blood Glucose ◽  
Regression Model ◽  
Influencing Factors ◽  
Logistic Model ◽  
Logistic Regression Model ◽  
Diabetes Management ◽  
Adaptive Lasso ◽  
Community Management ◽  
Diabetic Patients

Abstract Background: Influencing factors of community management of diabetes are complex and controversial, and how to select the most effective multiple influencing factors requires in-depth research. This study aims to analyse multiple influencing factors by adaptive-lasso logistic regression model for the effectiveness of diabetes management of the community to improve the efficiency and reduce the burden of diabetes. Methods: A cross-sectional survey (N=1,127) was adopted to establish the adaptive-lasso logistic regression model of influencing factors for community management of diabetic patients based on cluster sampling data of diabetic patients in Chengdu city, China. By comparing with the full-variable logistic model and the ridge logistic model to find the advantages of the adaptive lasso-logistic regression model in community diabetes management. Results: A total of 1,127 diabetic patients were included in the cross-sectional survey. The latest fasting blood glucose was included in the analysis. Among the included population, 90.6% of them had a fasting glucose level higher than 6.1mol/L, and 9.4% of them were below 6.1mol/L. By cross-validation, after folding eight times, the variables involved in the Adaptive lasso-logistic regression model include age, education level, main source of income, marital status, average monthly income, free medical service, basic medical insurance for residents, hospital history, number of follow-up evaluations by family doctor team, voluntary participation in community blood glucose measurement. The AIC and BIC criteria of adaptive lasso-logistic regression model were 2062 and 1981, which were lower than the full-variable logistic model (2349, 2023) and the ridge logistic model (2312, 2013). From the perspective of time cost, the adaptive-lasso logistic regression model was better than the other two models. Conclusions: The adaptive-lasso logistic regression model can be used to analyse the influencing factors of community management in patients with diabetes. Community intervention and intensive management measures can significantly improve the blood glucose status of patients with diabetes.

scholarly journals Rice Marketing as a Means of Poverty Alleviation in Niger State, Nigeria

2014 ◽  
Vol 47 (4) ◽  
pp. 137-141
Author(s):  
Eniola Oluwatoyin Olorunsanya ◽  
Josephine Utsunu Ugbong
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Poverty Alleviation ◽  
Poverty Reduction ◽  
Logistic Regression Model ◽  
Educational Status ◽  
Household Size ◽  
Descriptive Statistics ◽  
Poverty Measures ◽  
And Gender

Abstract This study examined rice marketing as a means of poverty alleviation in Niger State, North Central Nigeria. Ninety-eight representative rice marketers’ households were used for the study. Descriptive statistics, Foster, Greer and Thorbecke poverty measures as well as logistic regression model were used as the analytical tools for the study. The result of the descriptive statistics shows that forty-nine percent of the rice marketers have no western education and majority of the rice marketers’ households used open spaces for defecation. The result of the poverty profile shows that all the representative households were poor using 1.25 dollar a day poverty benchmark and only 32 percent were poor using the estimated relative poverty benchmark of N 1,894.2 per capita. The result of the logistic regression model shows the following factors influenced the poverty status of the rice marketers’ households in the study area. These are age and gender of the rice marketers, household size, other sources of income, marital status of the rice marketers and their educational status. The study recommends manageable household size as well as improved level of education for members of the rice marketers’ households for poverty reduction in the study area.

scholarly journals Association between Obesity and Asthma in Preschool Mexican Children

2010 ◽  
Vol 10 ◽  
pp. 1339-1346 ◽  
Author(s):  
Francisco Vázquez-Nava ◽  
Jaime Morales Romero ◽  
Arturo Córdova-Fernandez ◽  
Atenógenes H. Saldívar-González ◽  
Carlos F. Vázquez-Rodríguez ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Preschool Children ◽  
Regression Model ◽  
Logistic Regression Model ◽  
International Study ◽  
Normal Weight ◽  
The Body ◽  
Mexican Children ◽  
Prevalence Of Obesity ◽  
And Gender

The elevated prevalence of obesity as well as of asthma in preschool children has prompted investigators to speculate that obesity in childhood might be a causal factor in the development of asthma. The results obtained to date are debatable. We investigated the association between obesity and asthma in 1,160 preschool Mexican children. Diagnosis of asthma was performed using the International Study of Asthma and Allergy in Childhood (ISAAC) questionnaire. The body mass index (BMI) in units of kg/m2was determined, and children were categorized according to age- and gender-specific criteria, such as normal weight (5th-85thpercentile), overweight (ࣙ85thand <95thpercentile), and obesity (ࣙ95thpercentile). Power test for logistic regression model was calculated. We found no association between overweight (adjusted OR = 1.02; 95% CI = 0.66–1.58), obesity (adjusted OR = 0.94; 95% CI = 0.68–1.30), and wheezing during the last year as determined by logistic regression model adjusted. We did not find an association between overweight, obesity, and asthma-associated hospitalizations. Further longitudinal studies are required to provide a better understanding of the relationship between obesity and asthma in preschool children.

scholarly journals Analysis of Influencing Factors of Community Management of Diabetes Based on Adaptive-Lasso Logistic Regression Model: A Community-based Study

2021 ◽  
Author(s):  
Wei Lin ◽  
Yang Tian ◽  
Adeel Khoja ◽  
Xuan Zhao ◽  
Peng Hu ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Blood Glucose ◽  
Regression Model ◽  
Influencing Factors ◽  
Logistic Model ◽  
Logistic Regression Model ◽  
Information Criterion ◽  
Adaptive Lasso ◽  
Community Management ◽  
Patients With Diabetes

Abstract Background This study aimed to analyse which influencing factors may be more effective to achieve diabetes management targets in the community by the adaptive-lasso logistic regression model. Methods A cross-sectional study (N=1,127) was adopted to establish the adaptive-lasso logistic regression model of influencing factors for community management based on multi-stage cluster sampling data among patients with diabetes in China. Patient’s fasting blood glucose level, blood pressure, and triglycerides was collected. Results Overall, 90.6% of included people had a fasting glucose level higher than 6.1mol/L, and 9.4% of them were below 6.1mol/L. By cross-validation, after folding eight times, the variables involved in the adaptive lasso-logistic regression model include age, education level, main source of income, marital status, average monthly income, free medical service, basic medical insurance for residents, hospital history, number of follow-up evaluations by family doctor team, voluntary participation in community blood glucose measurement. The Akaike Information Criterion and Bayesian Information Criterion of adaptive lasso-logistic regression model were 1980 and 2021, which were lower than the full-variable logistic model (2041, 2245) and the ridge logistic model (2043, 2348). The adaptive-lasso logistic regression model was better than the other two models regarding time cost.Conclusions The adaptive-lasso logistic regression model can analyse the influencing factors of community management in patients with diabetes. Community intervention and intensive management measures can significantly improve the blood glucose status of patients with diabetes.

scholarly journals Machine Learning Models for Breast Lesions Based on Ultrasound Imaging Features: A Observational Study

2020 ◽  
Author(s):  
Yao Tan ◽  
Ling Huo ◽  
Shu Wang ◽  
Cuizhi Geng ◽  
Yi Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Regression Model ◽  
Ultrasound Imaging ◽  
Logistic Model ◽  
Logistic Regression Model ◽  
Support Vector ◽  
Imaging Features ◽  
Data Set ◽  
Class A

Abstract Background: The accuracy of breast cancer (BC) screening based on conventional ultrasound imaging examination largely depends on the experience of clinicians. Further, the effectiveness of BC screening and diagnosis in primary hospitals need to be improved. This study aimed to establish and evaluate the usefulness of a simple, practical, and easy-to-promote machine learning model based on ultrasound imaging features for diagnosing BC.Methods: Logistic regression, random forest, extra trees, support vector, multilayer perceptron, and XG boost models were developed. The modeling data set was divided into a training set and test set in a 75%:25% ratio, and these were used to establish the models and test their performance, respectively. The validation data set of primary hospitals was used for external validation of the model. The area under the receiver operating characteristic curve (AUC) was used as the main evaluation index, and pathological biopsy was used as the gold standard for evaluating each model. Diagnostic capability was also compared with those of clinicians. Results: Among the six models, the logistic model showed superior capability, with an AUC of 0.771 and 0.906 in the test and validation sets, respectively, and Brier scores of 0.18 and 0.165. The AUC of the logistic model in tertiary class A hospitals and primary hospitals was 0.875 and 0.921, respectively. The AUCs of the clinician diagnosis and the logistic model were 0.913 and 0.906. Their AUCs in the tertiary class A hospitals were 0.890 and 0.875, respectively, and were 0.924 and 0.921 in primary hospitals, respectively. Conclusions: The logistic regression model has better overall performance in primary hospitals, and the logistic regression model can be further extended to the basic level. A more balanced clinical prediction model can be further established on the premise of improving accuracy to assist clinicians in decision making and improve diagnosis.Trial Registration: http://www.clinicaltrials.gov. ClinicalTrials.gov ID: NCT03080623.

scholarly journals Prevalence and correlates of comprehensive HIV/AIDS knowledge among adolescent girls and young women aged 15–24 years in Malawi: evidence from the 2015–16 Malawi demographic and health survey

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Chrispin Mandiwa ◽  
Bernadetta Namondwe ◽  
Mtondera Munthali
Keyword(s):  
Public Health ◽  
Logistic Regression ◽  
Regression Model ◽  
Adolescent Girls ◽  
Young Women ◽  
Health Survey ◽  
Logistic Regression Model ◽  
Multivariable Logistic Regression Model ◽  
Aids Knowledge ◽  
Hiv Aids

Abstract Background HIV epidemic remains a major public health issue in Malawi especially among adolescent girls and young women (AGYW). Comprehensive HIV/AIDS knowledge (defined as correct knowledge of two major ways of preventing the sexual transmission of HIV and rejection of three misconceptions about HIV) is a key component of preventing new HIV infections among AGYW. Therefore, the aim of this study was to identify the correlates of comprehensive HIV/AIDS knowledge among AGYW in Malawi. Methods The study was based on cross-sectional data from the 2015–2016 Malawi Demographic and Health Survey. It involved 10,422 AGYW aged 15–24 years. The outcome variable was comprehensive HIV/AIDS knowledge. Data were analysed using descriptive statistics, bivariate and multivariable logistic regression model. All the analyses were performed using complex sample analysis procedure of the Statistical Package for Social Sciences to account for complex survey design. Results Approximately 42.2% of the study participants had comprehensive HIV/AIDS knowledge. Around 28% of the participants did not know that using condoms consistently can reduce the risk of HIV and 25% of the participants believed that mosquitoes could transmit HIV. Multivariable logistic regression model demonstrated that having higher education (AOR = 2.97, 95% CI: 2.35–3.75), belonging to richest households (AOR = 1.24, 95% CI: 1.05–1.45), being from central region (AOR = 1.65, 95% CI:1.43–1.89), southern region (AOR = 1.65, 95% CI: 1.43–1.90),listening to radio at least once a week (AOR = 1.27, 95% CI: 1.15–1.40) and ever tested for HIV (AOR = 1.88, 95% CI: 1.68–2.09) were significantly correlated with comprehensive HIV/AIDS knowledge. Conclusions The findings indicate that comprehensive HIV/AIDS knowledge among AGYW in Malawi is low. Various social-demographic characteristics were significantly correlated with comprehensive HIV/AIDS knowledge in this study. These findings suggest that public health programmes designed to improve comprehensive HIV/AIDS knowledge in Malawi should focus on uneducated young women, those residing in northern region and from poor households. There is also a need to target AGYW who have never tested for HIV with voluntary counselling and testing services. This measure might both improve their comprehensive HIV/AIDS knowledge and awareness of their health status.

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