scholarly journals Study on Accident Prediction Models in Urban Railway Casualty Accidents Using Logistic Regression Analysis Model

2017 ◽  
Vol 20 (4) ◽  
pp. 482-490
Author(s):  
Soo-Bong Jin ◽  
Jong-Woo Lee
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Prediction Models ◽  
Analysis Model ◽  
Accident Prediction

scholarly journals Decreased eGFR Is Associated With Ischemic Stroke in Patients With Dilated Cardiomyopathy

2019 ◽  
Vol 25 ◽  
pp. 107602961986690 ◽  
Author(s):  
Yuqing Deng ◽  
Zhiqing Chen ◽  
Lili Hu ◽  
Zhenyan Xu ◽  
Jinzhu Hu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Ischemic Stroke ◽  
Dilated Cardiomyopathy ◽  
Logistic Regression Analysis ◽  
Multivariate Logistic Regression Analysis ◽  
Analysis Model ◽  
Multivariate Logistic Regression ◽  
Increased Risk

Dilated cardiomyopathy (DCM) is increasingly indicated as a cause of cardioembolic syndrome, in particular, cardioembolic ischemia stroke. However, the potential risk factors for stroke among DCM patients remain under investigated. DCM patients hospitalized from June 2011 to June 2016 were included. The cases were defined as the group of DCM patients with stroke compared with those without stroke. Clinical characteristic data were collected and compared between the two groups including demographic data, complicated diseases, echocardiography index, and laboratory parameters and estimated glomerular filtration rate (eGFR). A multivariate logistic regression analysis model adjusted by sex and age was used to explore the related risk factors for stroke in DCM patients. A total of 779 hospitalized patients with DCM were included. Of these, 55 (7.1%) had experienced a stroke. Significantly lower eGFR levels (68.03 ± 26.22 vs 79.88 ± 24.25 mL/min/1.73 m2, P = .001) and larger left atrial diameters (45.32 ± 7.79 vs 43.25 ± 7.11 mm, P = .04) were found in the group of patients having DCM with stroke compared to those without stroke. When the eGFR was categorized as eGFR >60, 30<eGFR≤ 60 and eGFR ≤ 30, there were more patients with 30<eGFR≤ 60 (30.9% vs 17.7%) and eGFR≤ 30 (9.1% vs 3.3%) in the ischemic stroke group ( P = 0.003). A multivariate logistic regression analysis model adjusted by sex and age showed that 30 <eGFR≤60 (odds ratio [OR]: 2.07, 95% confidence interval [CI]: [1.05-4.07], P = .035) and eGFR≤30 (OR: 4.04, 95% CI: [1.41-11.62], P = .009) were statistically associated with ischemic stroke in patients with DCM. It is concluded that decreased eGFR is significantly associated with an increased risk of ischemic stroke in patients with DCM.

scholarly journals Failure prediction of government funded start-up firms

2017 ◽  
Vol 14 (2) ◽  
pp. 296-306 ◽  
Author(s):  
Oliver Lukason ◽  
Kaspar Käsper
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Prediction Model ◽  
Financial Development ◽  
Logistic Regression Analysis ◽  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Financial Ratios ◽  
Development Patterns ◽  
Start Up

This study aims to create a prediction model that would forecast the bankruptcy of government funded start-up firms (GFSUs). Also, the financial development patterns of GFSUs are outlined. The dataset consists of 417 Estonian GFSUs, of which 75 have bankrupted before becoming five years old and 312 have survived for five years. Six financial ratios have been calculated for one (t+1) and two (t+2) years after firms have become active. Weighted logistic regression analysis is applied to create the bankruptcy prediction models and consecutive factor and cluster analyses are applied to outline the financial patterns. Bankruptcy prediction models obtain average classification accuracies, namely 63.8% for t+1 and 67.8% for t+2. The bankrupt firms are distinguished with a higher accuracy than the survived firms, with liquidity and equity ratios being the useful predictors of bankruptcy. Five financial patterns are detected for GFSUs, but bankrupt GFSUs do not follow any distinct patterns that would be characteristic only to them.

scholarly journals The Diagnosis Value of a Novel Model with 5 Circulating miRNAs for Liver Fibrosis in Patients with Chronic Hepatitis B

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Qingqing Zhang ◽  
Qidi Zhang ◽  
Binghang Li ◽  
Ying Qu ◽  
Zhenghong Li ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Chronic Hepatitis ◽  
Liver Fibrosis ◽  
Hepatitis B ◽  
Chronic Hepatitis B ◽  
Logistic Regression Analysis ◽  
Prediction Models ◽  
Expression Profiles ◽  
Molecular Diagnostic

Background and Aim. miRNAs play an important role in the development of human fibrosis. However, miRNA expression profiles during different stages of chronic hepatitis B (CHB) are not well defined. In this study, we aimed to further validate the features of 5 miRNA candidates from our previous study during different fibrotic stages and the value of diagnosis for liver fibrosis. Methods. Differential expression of five selected miRNAs (hsa-mir-1225-3p, hsa-mir-1238, hsa-miR-3162-3P, hsa-miR-4721, and hsa-miR-H7) was verified by qRT-PCR in the plasma of 83 patients and 20 healthy controls. The relative expression of these miRNAs was analyzed in different groups to screen target miRNA. A logistic regression analysis was performed to assess factors associated with fibrosis progression. The receiver operating characteristic (ROC) curve and discriminant analyses validated the ability of these predicted variables to discriminate the nonsignificant liver fibrosis group from the significant liver fibrosis group. Furthermore, the established models were compared with other prediction models to evaluate the diagnostic efficiency. Results. These five tested miRNAs all had signature correlations with hepatic fibrotic level ( p < 0.05 ), and the upregulation trends were consistent with miRNA microarray analysis previously. The multivariate logistic regression analysis identified that a model of five miRNAs (miR-5) had a high diagnostic accuracy in discrimination of different stages of liver fibrosis. The ROC showed that the miR-5 has excellent value in diagnosis of fibrosis, even better than the Forns score, FIB-4, S index, and APRI. GO functions of different miRNAs mainly involved in various biological processes were markedly involved in HBV and revealed signaling pathways dysregulated in liver fibrosis of CHB patients. Conclusions. It was validated that the combination of these five miRNAs was a new set of promising molecular diagnostic markers for liver fibrosis. The diagnosis model (miR-5) can distinguish significant and nonsignificant liver fibrosis with high sensitivity and specificity.

scholarly journals THE RELATIONSHIP BETWEEN FAMILY FUNCTIONS AND RISKY SEXUAL BEHAVIOR IN INDONESIA IN 2018: A PMA DATA ANALYSIS

2021 ◽  
Vol 10 (1) ◽  
pp. 94
Author(s):  
Diah Puspita Sari ◽  
Mario Ekoriano ◽  
Aditya Rahmadhony
Keyword(s):  
Logistic Regression ◽  
Sexual Behavior ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Sexual Behaviors ◽  
Risky Sexual Behavior ◽  
Age Groups ◽  
Adolescent Sexual Behavior ◽  
Analysis Model ◽  
The Relationship

This analysis aimed to examine the relationship between family development and risky adolescent sexual behavior in Indonesia. The data were taken from the 2018 Performance and Accountability Survey with a family and adolescent questionnaire; thus, the relationship between parents and adolescents could be identified. The statistical analysis methods used were descriptive and inferential analyses, with the unit of analysis being 15,556 teenagers who dated. The results of the logistic regression analysis (Model 1) by using all the independent variables simultaneously found that sexual risk behavior was mostly found in in boys, age categories 20-24 years, and participants who never had access to PIK-R. Risky sexual behavior was also dependent on whether participants agreed to have relations sexual before marriage, and also the level of education of their parents. The logistic regression analysis (Model 2) found residence, gender, level of adolescent education, age categories, agreement to have sexual relations before marriage, age groups of head of family, gender of head of family, education level of head of family, work status of head of family, economic status, and the activeness of the BKR activities contributed to adolescents committing risky sexual behaviors.

scholarly journals Personality, Gender, Culture, and Entrepreneurial Intentions of Undergraduate Student: Binary Logistic Regression

2020 ◽  
Vol 8 (2) ◽  
pp. 128-143
Author(s):  
Bambang Mardisetosa ◽  
Khusaini Khusaini ◽  
Gumelar Widia Asmoro
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Binary Logistic Regression ◽  
Entrepreneurial Intention ◽  
Entrepreneurial Intentions ◽  
Measurement Scale ◽  
Dominant Factor ◽  
Binary Logistic Regression Analysis ◽  
Analysis Model

In the last two decades, empirical studies on entrepreneurship had been increasing, especially the object of study of student entrepreneurial intention. The objective of this study was to examine the empirical evidence about significant factors and the most dominant factor (having the highest rank) influencing entrepreneurial intention of Islamic Shekh-Yusuf University students. This study used a descriptive quantitative approach that was a binary logistic regression analysis model with a sample of 382 respondents from a population of 4.058 students. Data obtained using a questionnaire tested for validity and reliability. Measurement scale used a Likert scale, and the distribution of questionnaire was accidental sampling technique. Based on binary logistic regression analysis, it showed that personality and situational significantly influenced students entreprenurial intentions, while culture and gender were not significant. The contribution of this research was as input to the university about the importance of developing students' entrepreneurial interets and talents so that graduates could contribute to reducing unemployment.  

Ketepatan Altman Score, Zmijewski Score, Grover Score, dan Fulmer Score dalam menentukan Financial Distress pada Perusahaan Trade and Service

Owner ◽  
2019 ◽  
Vol 3 (2) ◽  
pp. 278
Author(s):  
Munawarah Munawarah ◽  
Anton Wijaya ◽  
Cindy Fransisca ◽  
Felicia Felicia ◽  
Kavita Kavita
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Prediction Model ◽  
Logistic Regression Analysis ◽  
Financial Distress ◽  
Prediction Models ◽  
Stock Exchange ◽  
Financial Reports ◽  
Service Company ◽  
A Value

This research purpose to determine the accuracy among Altman, Zmijewski, Grover, and the Fulmer models in predicting financial distress, and to determine the most accurate prediction models to use in Trade and Service company. With the accuracy of the overall prediction model of 89.4%, this research will compare the four prediction models using real conditions of the company. The Data that used in this research are all form of annual financial reports published by companies on the Indonesia Stock Exchange website. The population used is Trade and Service’s company listed on the Indonesia Stock Exchange for the period 2013-2017. Purposive sampling used in this research to obtain 34 companies as research sample. This research compares four prediction models of financial distress using logistic regression analysis. According to the result of this research shows the accuracy between the Altman, Zmijewski, Grover, and Fulmer models to predict financial distress, which the highest level of accuracy is achieved by Zmijewski model and Fulmer model with a value of 100%, followed by Grover model with a value of 97% while Altman model with a value of 73,5%.

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