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.

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 The Factors Affecting Eye Patients (Cataract) In Jordan by Using the Logistic Regression Model

2017 ◽  
Vol 11 (8) ◽  
pp. 38
Author(s):  
Adeeb Ahmed Ali AL Rahamneh ◽  
Omar M. Hawamdeh
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Likelihood Ratio Test ◽  
Logistic Regression Model ◽  
Ratio Test ◽  
Response Variable ◽  
Factors Affecting ◽  
Independent Variables ◽  
Lemeshow Test ◽  
The Right

This study aims to use the logistic regression model to classify patients as infected and without cataracts. The independent variables were used to represent the gender, the age, the pressure in the right eye, the pressure in the left eye, HbA1C, and the anemia, representative variables for the study of Cataract disease affects the eyes, based on a random sample of (116) patients. The results proved that the used logistic regression model is an efficient and representative for data that shows through (Likelihood Ratio Test) and (Hosmer and Lemeshow test), and the study proved that the value of (R Square Nagelkerke=1) this means that 100% of the change in the occurred changes in the response variable explained through the Logistic regression model.

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.

scholarly journals Comparison Study of Logistic Regression Model for Albanian Texts

2016 ◽  
Vol 12 (9) ◽  
pp. 6572-6575
Author(s):  
Denisa Salillari ◽  
Luela Prifti
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Multinomial Logistic Regression ◽  
Classification Problem ◽  
Comparison Study ◽  
Multinomial Logistic Regression Model ◽  
Independent Variables ◽  
Predicted Probability ◽  
The Right

Considering authorship attribution as a classification problem we attempt to estimate the probability to find the right author for each text under study. In this paper using R we first improve the simple model for six Albanian texts, (I) increasing number of texts and number of independent variables and then compare the results taken with them of the multinomial logistic regression (II). The model was applied on a set of one hundred texts of ten different authors. For all the authors under study the average correct predicted probability is 0.918. Analyzing data from different Albanian texts, results that about 40% of their letters consist of vowels. As conclusion comparing results taken with them of (II) multinomial logistic regression model for Albanian texts has more advantages than logistic regression model.

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 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.

Knowledge sharing practice and associated factors among healthcare providers at University of Gondar comprehensive specialized hospital in Amhara region, North West Ethiopian, 2020. (Preprint)

2020 ◽  
Author(s):  
Addisalem Workie
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Knowledge Sharing ◽  
Associated Factors ◽  
Logistic Regression Model ◽  
Healthcare Providers ◽  
Multivariable Logistic Regression Model ◽  
Multivariable Logistic Regression ◽  
Specialized Hospital ◽  
The Right

BACKGROUND In healthcare, information and knowledge needs of health care providers arise in the patient care process. However, the right information and knowledge at the right time and place to the right person is not reached so far yet. Thus, leads to miss diagnosis, wastage of medical resources and knowledge. OBJECTIVE The objective of this study was to assess knowledge sharing practice and associated factors among healthcare providers at University of Gondar comprehensive specialized hospital. METHODS Institutional based cross-sectional study design was conducted through stratified simple random sampling technique among 423 samples from February 24 up to March 27, 2020. Pretested, self administered questionnaire were used to collect the required data. Epi info version 7 and stata software version 15 were used for data entry, processing and analysis respectively. Descriptive statistics and multivariable logistic regression model were applied to describe the study objects and to assess knowledge sharing practice and its associated factors. P value ≤ 0.05 were considered as factors associated with knowledge sharing practice. RESULTS In this study, 423 respondents were participated. From those participants, the level of knowledge sharing practice among healthcare providers was 65.0% (95% CI: 60.46-69.56) with 100% response rate. In multivariable logistic regression model awareness AOR=2.44, 95% CI= [1.32-4.50], willingness AOR=1.96, 95% CI= [1.10-3.53], perceived loss of knowledge power AOR=0.192, 95% CI= [.12-.32], the availability of health information resource AOR=2.00, 95% CI= [1.56-5.38] and opportunity AOR=2.91, 95% CI= [1.71-4.95] were significantly associated with KSP. CONCLUSIONS Knowledge sharing practice of healthcare provider was 65.01% which is higher as compared with most studies conducted in Ethiopia. But, it needs further opportunity, resource allocation and supportive leadership to make it more beyond what exist right now.

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.

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