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 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.

Die Flexion der Indefinita jemand und niemand

2021 ◽  
Vol 49 (2) ◽  
pp. 209-243
Author(s):  
Linnéa Weitkamp
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Influencing Factors ◽  
Logistic Regression Model ◽  
Binary Logistic Regression ◽  
Binary Logistic Regression Model ◽  
Corpus Study ◽  
Frequent Use ◽  
Current Variation ◽  
Reference Corpus

Abstract This article investigates the inflection of the German indefinite pronouns jemand and niemand in the accusative and dative. The pronouns are used both with inflectional suffix (jemanden/jemandem, niemanden/niemandem) and without (jemand, niemand) and are thus an example of current variation in contemporary German. The grammars take an unusually liberal stance and describe both forms as correct, partially even with preference to the uninflected form. A corpus study which examines conceptually written data of the DeReKo (German reference corpus) and conceptually oral data of the DECOW16B (German web corpus), shows that over 90 % of occurrences are inflected. But almost 10 % of uninflected forms show that these formations are no arbitrary errors either. To find out what influences the presence or absence of the inflectional ending, a binary logistic regression model was calculated. The following factors proved to be significant influencing factors for inflection: the degree of formality (DeReKo vs. DECOW16B), the lexeme (jemand vs. niemand), the case (acc vs. dat), government by preposition vs. government by verb and the following nominalized adjective (jemand anderen). With regard to the different inflectional suffixes, the frequent use of -en in the dative stood out in particular. Although this form is classified as erroneous in all grammars, almost 30 % of the dative occurrences in informal DECOW16B data are formed in this way.

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 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 Knowledge and Attitude on Insulin Self-Administration among Type 1 Diabetic Patients at Metu Karl Referral Hospital, Ethiopia

2019 ◽  
Vol 2019 ◽  
pp. 1-7
Author(s):  
Tewodros Yosef
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Referral Hospital ◽  
Favorable Attitude ◽  
Diabetic Patients ◽  
Self Administration ◽  
Knowledge And Attitude ◽  
Type 1 Diabetic Patients

Background. Diabetes mellitus (DM) is a group of metabolic diseases characterized by hyperglycemia resulting from defects in insulin secretion, insulin action, or both. It is a public health problem as the disease is epidemic in both developed and developing counties. Knowledge and attitude of patients regarding insulin self-administration could lead to better management of diabetes and eventually a good quality of life. Despite this, the evidence that showed the knowledge and attitude on insulin self-administration is a substantial deficiency in Ethiopia. Objective. To assess the level of knowledge, attitude, and associated factors on insulin self-administration among type 1 diabetic patients at Metu Karl Referral Hospital, Ethiopia, in 2019. Methods. An institutional-based cross-sectional study was conducted among systematically selected 245 type 1 diabetic patients at Metu Karl Referral Hospital, Ethiopia, in January 2019. The data were collected through a face-to-face interview. The collected data were entered using EpiData version 4.2.0.0, cleaned, and analyzed using SPSS version 20. A binary logistic regression model was used. Independent variables with a P value of less than 0.05 in the multivariable logistic regression model were considered significant. Results. Out of 242 type 1 diabetic patients interviewed, 93 (38.4%, 95% CI (32.3%-44.5%)) had good knowledge and 50 (20.7%, 95% CI (15.6%-25.8%)) had favorable attitude on insulin self-administration. The study also found that being unmarried (AOR=3.59, 95% CI (1.15-11.3), P=0.028), increased educational level (AOR=3.02, 95% CI (1.36-6.74), P=0.007), and more years of treatment (AOR=3.70, 95% CI (1.16-11.8), P=0.027) were factors associated with good knowledge on insulin self-administration, whereas being a member of DM association (AOR=3.57, 95% CI (1.66-7.69), P=0.001) was the only factor associated with favorable attitude on insulin self-administration. Conclusion. The knowledge and attitude on insulin self-administration among type 1 diabetic patients were substantially low. Diabetes and insulin self-administration education should be imparted by health professionals at each follow-up visit. Besides, strengthening of information, education, and communication (IEC) on the issue of diabetes and insulin self-administration using mass media (television/radio) plays paramount importance.

scholarly journals Insulin Resistance and Contrainsular Response in Type 2 Diabetes Mellitus Patients with Acute Coronary Syndrome

2020 ◽  
Vol 8 (B) ◽  
pp. 943-948
Author(s):  
Dinara Sheryazdanova ◽  
Yelena M. Laryushina ◽  
Larissa Muravlyova ◽  
Lyudmila G. Turgunova ◽  
Assel R. Alina ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Insulin Resistance ◽  
Acute Coronary Syndrome ◽  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Diabetic Patients ◽  
Positive Correlation ◽  
Coronary Syndrome

BACKGROUND: The number of patients with diabetes mellitus (DM) is progressively increasing all over the world. Over the past three decades, the global burden of diabetes has increased from 30 million in 1985 to 382 million in 2015, and current trends indicate that the prevalence of diabetes grows progressively. The phenomenon of insulin resistance established in the majority of type 2 DM (T2DM) patients. T2DM is associated with β-cell deficiency, α-cell resistance to insulin, and reduced effects of incretin. However, the role of insulin and glucagon in the process of cardiovascular complications in diabetic patients is a matter of debate. AIM: Our study aims to estimate insulin resistance and the contrainsular response in patients with T2DM and acute coronary syndrome (ACS). METHODS: The 104 T2DM patients aged 18–70 years participated in the observational study carried out in the Karaganda regional cardiosurgery hospital and ambulatory. The first group included 37 patients hospitalized for ACS in the first 24 h of admission. The second group included 67 patients without ACS. Determination of insulin resistance and contrainsular response was provided using a multiplex immunological assay with XMap technology on Bioplex 3D. RESULTS: During the research, we have discovered a decreased level of glucagon and increased homeostasis model assessment of insulin resistance (HOMA-IR) in patients with T2DM diabetes and ACS. Evaluation of traditional correlation interactions of HOMA-IR and indicators of carbohydrate metabolism showed a positive correlation with fasting plasma glucose in both study groups (Group 1: R = 0.47, p = 0.003; Group 2: R = 0.41, p = 0.024). Glucagon-like peptide (GLP)-1 has a weak positive correlation with HOMA-IR only in the first group (R = 0.32, p = 0.006). Increased insulin resistance was associated with high GLP-1 levels and low glucagon. The logistic regression model established that an increased HOMA-IR index rises the chance of ACS by 10.6% (OR = 1.106 [95% CI 1.105–1.206], p = 0,021). The logistic regression model, reflecting the relation between glucagon and ACS, shows that increased glucagon reduces the ACS odds (OR = 0.989 [95% CI 0.979–0.999], p = 0.026). The adjusted regression model showed no significant influence of early presented factors on the probability of ACS. CONCLUSION: There is a trend toward elevated HOMA-IR insulin resistance index and decreased level of glucagon in diabetic patients with ACS.

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