scholarly journals Statistical Modeling via Bootstrapping and Weighted Techniques Based on Variances

2018 ◽  
Vol 8 (4) ◽  
pp. 3135-3140
Author(s):  
W. M. A. W. Ahmad ◽  
N. A. Aleng ◽  
Z. Ali ◽  
M. S. M. Ibrahim
Keyword(s):  
Logistic Regression ◽  
Probability Model ◽  
Bootstrap Method ◽  
Statistical Technique ◽  
Multiple Logistic Regression ◽  
Statistical Inferences ◽  
Sas Macro ◽  
Real Scenario ◽  
Logistic Regression Algorithm ◽  
Bayesian Logistic Regression

Multiple logistic regression is a methodology of handling dependent variables with a binary outcome. This method is becoming increasingly widespread as a statistical technique that represents a discrete probability model. Many studies have focused on the application but less on the methodology building. This study aims to provide an applied method for multiple logistic regression which is called modified Bayesian logistic regression modeling as an alternative technique for logistic regression analysis that focuses on a combination of the bootstrap method using SAS macro and weighted techniques based on variances using SAS algorithm. Data on oral cancer were applied to illustrate a real scenario of oral health data. This data will be applied to the multiple logistic regression algorithm and modified Bayesian logistic regression. Results from both cases are strongly supported by clinical studies. Through the proposed algorithm, the researcher will have an option whether to analyze the data with the usual or an alternative method. Final results indicate that the modified procedure can provide more efficient results especially for the case which involves statistical inferences.

Prediction Model of Severe Coronavirus Disease 2019(COVID-19) Cases Shows the Leading Risk Factor of Hypocalcemia

2020 ◽  
Author(s):  
Chenchan Hu ◽  
Feifei Su ◽  
Jianyi Dai ◽  
Shushu Lu ◽  
Lianpeng Wu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Prediction Model ◽  
Probability Model ◽  
Multiple Logistic Regression Analysis ◽  
Treatment Programs ◽  
Multiple Logistic Regression ◽  
High Transmission ◽  
Transmission Setting

Abstract Background A striking characteristic of Coronavirus Disease 2019(COVID-19) is the coexistence of clinically mild and severe cases. A comprehensive analysis of multiple risk factors predicting progression to severity is clinically meaningful. Methods The patients were classified into moderate and severe groups. The univariate regression analysis was used to identify their epidemiological and clinical features related to severity, which were used as possible risk factors and were entered into a forward-stepwise multiple logistic regression analysis to develop a multiple factor prediction model for the severe cases.Results 255 patients (mean age, 49.1±SD 14.6) were included, consisting of 184 (72.2%) moderate cases and 71 (27.8%) severe cases. The common symptoms were dry cough (78.0%), sputum (62.7%), and fever (59.2%). The less common symptoms were fatigue (29.4%), diarrhea (25.9%), and dyspnea (20.8%). The univariate regression analysis determined 23 possible risk factors. The multiple logistic regression identified seven risk factors closely related to the severity of COVID-19, including dyspnea, exposure history in Wuhan, CRP (C-reactive protein), aspartate aminotransferase (AST), calcium, lymphocytes, and age. The probability model for predicting the severe COVID-19 was P=1/1+exp (-1.78+1.02×age+1.62×high-transmission-setting-exposure +1.77×dyspnea+1.54×CRP+1.03×lymphocyte+1.03×AST+1.76×calcium). Dyspnea (OR=5.91) and hypocalcemia (OR=5.79) were the leading risk factors, followed by exposure to a high-transmission setting (OR=5.04), CRP (OR=4.67), AST (OR=2.81), decreased lymphocyte count (OR=2.80), and age (OR=2.78). Conclusions This quantitative prognosis prediction model can provide a theoretical basis for the early formulation of individualized diagnosis and treatment programs and prevention of severe diseases.

Determining the Most Valuable Clinical Variables : a Stepwise Multiple Logistic Regression Program

1980 ◽  
Vol 19 (01) ◽  
pp. 42-49 ◽  
Author(s):  
B. W. Brown ◽  
C. Engelhard ◽  
J. Haipern ◽  
J. F. Fries ◽  
L. S. Coles
Keyword(s):  
Logistic Regression ◽  
Predictor Variable ◽  
Clinical Problem ◽  
Relevant Information ◽  
Stepwise Logistic Regression ◽  
Multiple Logistic Regression ◽  
Binary Outcome ◽  
Medical Problems ◽  
Valuable Predictor ◽  
Data Banks

In solving a clinical problem of diagnosis, prognosis, or treatment choice, a physician must select from among a large group of possible tests. In general, an ordering exists specifying which tests are most valuable in providing relevant information concerning the problem on hand. The computer program package to be described (MW) extracts appropriate data from the ARAMIS data banks and then analyzes the data by stepwise logistic regression. A binary outcome (diagnosis, prognostic event, or treatment response) is sequentially associated with possible tests, and the most powerful combination of tests is identified. For example, the most valuable predictor variable of early mortality in SLE is proteinuria, followed sequentially by anemia and absence of arthritis. Experience with these techniques suggests : 1. optimal certainty is usually reached after only three or four tests; 2. several different test sequences may lead to the same level of certainty; 3. diagnosis may usually be ascertained with greater certainty than prognosis; 4. many medical problems contain considerable non-reducible uncertainty; 5. a relatively small group of tests are typically found among the most powerful; 6. results are consistent across several patient populations; 7. results are largely independent of the particular statistic employed. These observations suggest strategies for maximizing information while minimizing risk and expense.

Experimental study on the factors affecting squeak noise occurrence in automotive suspension bushings

2021 ◽  
pp. 095440702110241
Author(s):  
Byunghyun Kang ◽  
Cheol Choi ◽  
Daeun Sung ◽  
Seongho Yoon ◽  
Byoung-Ho Choi
Keyword(s):  
Neural Network ◽  
Logistic Regression ◽  
Influencing Factors ◽  
Frictional Force ◽  
Prediction Models ◽  
Statistical Tests ◽  
Network Models ◽  
Multiple Logistic Regression ◽  
Neural Network Models ◽  
Automotive Suspension

In this study, friction tests are performed, via a custom-built friction tester, on specimens of natural rubber used in automotive suspension bushings. By analyzing the problematic suspension bushings, the eleven candidate factors that influence squeak noise are selected: surface lubrication, hardness, vulcanization condition, surface texture, additive content, sample thickness, thermal aging, temperature, surface moisture, friction speed, and normal force. Through friction tests, the changes are investigated in frictional force and squeak noise occurrence according to various levels of the influencing factors. The degree of correlation between frictional force and squeak noise occurrence with the factors is determined through statistical tests, and the relationship between frictional force and squeak noise occurrence based on the test results is discussed. Squeak noise prediction models are constructed by considering the interactions among the influencing factors through both multiple logistic regression and neural network analysis. The accuracies of the two prediction models are evaluated by comparing predicted and measured results. The accuracies of the multiple logistic regression and neural network models in predicting the occurrence of squeak noise are 88.2% and 87.2%, respectively.

scholarly journals Google Play Content Scraping and Knowledge Engineering using Natural Language Processing Techniques with the Analysis of User Reviews

2020 ◽  
Vol 30 (1) ◽  
pp. 192-208 ◽  
Author(s):  
Hamza Aldabbas ◽  
Abdullah Bajahzar ◽  
Meshrif Alruily ◽  
Ali Adil Qureshi ◽  
Rana M. Amir Latif ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Language Processing ◽  
Mobile Application ◽  
Knowledge Engineering ◽  
Machine Learning Algorithms ◽  
Application Development ◽  
User Reviews ◽  
N Gram ◽  
Logistic Regression Algorithm ◽  
Google Play

Abstract To maintain the competitive edge and evaluating the needs of the quality app is in the mobile application market. The user’s feedback on these applications plays an essential role in the mobile application development industry. The rapid growth of web technology gave people an opportunity to interact and express their review, rate and share their feedback about applications. In this paper we have scrapped 506259 of user reviews and applications rate from Google Play Store from 14 different categories. The statistical information was measured in the results using different of common machine learning algorithms such as the Logistic Regression, Random Forest Classifier, and Multinomial Naïve Bayes. Different parameters including the accuracy, precision, recall, and F1 score were used to evaluate Bigram, Trigram, and N-gram, and the statistical result of these algorithms was compared. The analysis of each algorithm, one by one, is performed, and the result has been evaluated. It is concluded that logistic regression is the best algorithm for review analysis of the Google Play Store applications. The results have been checked scientifically, and it is found that the accuracy of the logistic regression algorithm for analyzing different reviews based on three classes, i.e., positive, negative, and neutral.

Association between Procalcitonin and Acute Kidney Injury in Patients with Bacterial Septic Shock

2021 ◽  
pp. 1-10
Author(s):  
Guang Fu ◽  
Hai-chao Zhan ◽  
Hao-li Li ◽  
Jun-fu Lu ◽  
Yan-hong Chen ◽  
...  
Keyword(s):  
Septic Shock ◽  
Acute Kidney Injury ◽  
Logistic Regression ◽  
Piecewise Linear ◽  
Kidney Injury ◽  
Multiple Logistic Regression ◽  
Primary Analysis ◽  
Female Patients ◽  
Potential Biomarker ◽  
The Relationship

Objective: The objective of this study was to assess the relationship between serum procalcitonin (PCT) and acute kidney injury (AKI) induced by bacterial septic shock. Methods: A retrospective study was designed which included patients who were admitted to the ICU from January 2015 to October 2018. Multiple logistic regression and receiver operating characteristic (ROC) as well as smooth curve fitting analysis were used to assess the relationship between the PCT level and AKI. Results: Of the 1,631 patients screened, 157 patients were included in the primary analysis in which 84 (53.5%) patients were with AKI. Multiple logistic regression results showed that PCT (odds ratio [OR] = 1.017, 95% confidence interval [CI] 1.009–1.025, p < 0.001) was associated with AKI induced by septic shock. The ROC analysis showed that the cutoff point for PCT to predict AKI development was 14 ng/mL, with a sensitivity of 63% and specificity 67%. Specifically, in multivariate piecewise linear regression, the occurrence of AKI decreased with the elevation of PCT when PCT was between 25 ng/mL and 120 ng/mL (OR 0.963, 95% CI 0.929–0.999; p = 0.042). The AKI increased with the elevation of PCT when PCT was either <25 ng/mL (OR 1.077, 95% CI 1.022–1.136; p = 0.006) or >120 ng/mL (OR 1.042, 95% CI 1.009–1.076; p = 0.013). Moreover, the PCT level was significantly higher in the AKI group only in female patients aged ≤75 years (p = 0.001). Conclusions: Our data revealed a nonlinear relationship between PCT and AKI in septic shock patients, and PCT could be used as a potential biomarker of AKI in female patients younger than 75 years with bacterial septic shock.

scholarly journals Right choice of a method for determination of cut-off values: A statistical tool for a diagnostic test

2014 ◽  
Vol 5 (3) ◽  
pp. 30-34 ◽  
Author(s):  
Balkishan Sharma ◽  
Ravikant Jain
Keyword(s):  
Logistic Regression ◽  
Discriminant Analysis ◽  
Diagnostic Test ◽  
Roc Curve ◽  
Traditional Method ◽  
Statistical Technique ◽  
Curve Analysis ◽  
Operating Characteristics ◽  
Roc Curve Analysis

Objective: The clinical diagnostic tests are generally used to identify the presence of a disease. The cutoff value of a diagnostic test should be chosen to maximize the advantage that accrues from testing a population of human and others. When a diagnostic test is to be used in a clinical condition, there may be an opportunity to improve the test by changing the cutoff value. To enhance the accuracy of diagnosis is to develop new tests by using a proper statistical technique with optimum sensitivity and specificity. Method: Mean±2SD method, Logistic Regression Analysis, Receivers Operating Characteristics (ROC) curve analysis and Discriminant Analysis (DA) have been discussed with their respective applications. Results: The study highlighted some important methods to determine the cutoff points for a diagnostic test. The traditional method is to identify the cut-off values is Mean±2SD method. Logistic Regression Analysis, Receivers Operating Characteristics (ROC) curve analysis and Discriminant Analysis (DA) have been proved to be beneficial statistical tools for determination of cut-off points.Conclusion: There may be an opportunity to improve the test by changing the cut-off value with the help of a correctly identified statistical technique in a clinical condition when a diagnostic test is to be used. The traditional method is to identify the cut-off values is Mean ± 2SD method. It was evidenced in certain conditions that logistic regression is found to be a good predictor and the validity of the same can be confirmed by identifying the area under the ROC curve. Abbreviations: ROC-Receiver operating characteristics and DA-Discriminant Analysis. Asian Journal of Medical Science, Volume-5(3) 2014: 30-34 http://dx.doi.org/10.3126/ajms.v5i3.9296      

Sign in / Sign up

Export Citation Format

Share Document