scholarly journals Prediction model of algal blooms using logistic regression and confusion matrix

2021 ◽  
Vol 11 (3) ◽  
pp. 2407
Author(s):  
Hongwon Yun
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Prediction Model ◽  
Logistic Regression Analysis ◽  
Algal Blooms ◽  
Statistical Tests ◽  
Confusion Matrix ◽  
Factors Affecting ◽  
Predicted Values ◽  
Marine Environmental

Algal blooms data are collected and refined as experimental data for algal blooms prediction. Refined algal blooms dataset is analyzed by logistic regression analysis, and statistical tests and regularization are performed to find the marine environmental factors affecting algal blooms. The predicted value of algal bloom is obtained through logistic regression analysis using marine environment factors affecting algal blooms. The actual values and the predicted values of algal blooms dataset are applied to the confusion matrix. By improving the decision boundary of the existing logistic regression, and accuracy, sensitivity and precision for algal blooms prediction are improved. In this paper, the algal blooms prediction model is established by the ensemble method using logistic regression and confusion matrix. Algal blooms prediction is improved, and this is verified through big data analysis.

scholarly journals Predicting Burn Mortality Using a Simple Novel Prediction Model

2021 ◽  
Author(s):  
Sneha Sharma ◽  
Raman Tandon
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Prediction Model ◽  
Receiver Operating Characteristic ◽  
Logistic Regression Analysis ◽  
Operating Characteristic ◽  
Binary Logistic Regression ◽  
Apache Ii ◽  
Apache Ii Score ◽  
Binary Logistic Regression Analysis

Abstract Background Prediction of outcome for burn patients allows appropriate allocation of resources and prognostication. There is a paucity of simple to use burn-specific mortality prediction models which consider both endogenous and exogenous factors. Our objective was to create such a model. Methods A prospective observational study was performed on consecutive eligible consenting burns patients. Demographic data, total burn surface area (TBSA), results of complete blood count, kidney function test, and arterial blood gas analysis were collected. The quantitative variables were compared using the unpaired student t-test/nonparametric Mann Whitney U-test. Qualitative variables were compared using the ⊠2-test/Fischer exact test. Binary logistic regression analysis was done and a logit score was derived and simplified. The discrimination of these models was tested using the receiver operating characteristic curve; calibration was checked using the Hosmer—Lemeshow goodness of fit statistic, and the probability of death calculated. Validation was done using the bootstrapping technique in 5,000 samples. A p-value of <0.05 was considered significant. Results On univariate analysis TBSA (p <0.001) and Acute Physiology and Chronic Health Evaluation II (APACHE II) score (p = 0.004) were found to be independent predictors of mortality. TBSA (odds ratio [OR] 1.094, 95% confidence interval [CI] 1.037–1.155, p = 0.001) and APACHE II (OR 1.166, 95% CI 1.034–1.313, p = 0.012) retained significance on binary logistic regression analysis. The prediction model devised performed well (area under the receiver operating characteristic 0.778, 95% CI 0.681–0.875). Conclusion The prediction of mortality can be done accurately at the bedside using TBSA and APACHE II score.

Multivariable Logistic Regression Equation to Predict Prostate Cancer

2021 ◽  
Author(s):  
Lu Ma ◽  
Dong Cheng ◽  
Qinghua Li ◽  
Jingbo Zhu ◽  
Yu Wang ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Prediction Model ◽  
Prostate Specific Antigen ◽  
Sensitivity And Specificity ◽  
Logistic Regression Analysis ◽  
Roc Curve ◽  
Specific Antigen ◽  
Clinical Feature

Abstract Objective: To explore the predictive value of white blood cell (WBC), monocyte (M), neutrophil-to-lymphocyte ratio (NLR), fibrinogen (FIB), free prostate-specific antigen (fPSA) and free prostate-specific antigen/prostate-specific antigen (f/tPSA) in prostate cancer (PCa).Materials and methods: Retrospective analysis of 200 cases of prostate biopsy and collection of patients' systemic inflammation indicators, biochemical indicators, PSA and fPSA. First, the dimensionality of the clinical feature parameters is reduced by the Lass0 algorithm. Then, the logistic regression prediction model was constructed using the reduced parameters. The cut-off value, sensitivity and specificity of PCa are predicted by the ROC curve analysis and calculation model. Finally, based on Logistic regression analysis, a Nomogram for predicting PCa is obtained.Results: The six clinical indicators of WBC, M, NLR, FIB, fPSA, and f/tPSA were obtained after dimensionality reduction by Lass0 algorithm to improve the accuracy of model prediction. According to the regression coefficient value of each influencing factor, a logistic regression prediction model of PCa was established: logit P=-0.018-0.010×WBC+2.759×M-0.095×NLR-0.160×FIB-0.306×fPSA-2.910×f/tPSA. The area under the ROC curve is 0.816. When the logit P intercept value is -0.784, the sensitivity and specificity are 72.5% and 77.8%, respectively.Conclusion: The establishment of a predictive model through Logistic regression analysis can provide more adequate indications for the diagnosis of PCa. When the logit P cut-off value of the model is greater than -0.784, the model will be predicted to be PCa.

scholarly journals Factors Affecting Palm Farmers’ Decisions to Dispense Plantage-Product Zakat in Labuhanbatu Selatan Regency

2018 ◽  
Vol 3 (3) ◽  
pp. 13-24
Author(s):  
Amalia Agustin Syn ◽  
Khalifah Muhammad Ali ◽  
Didin Hafidhuddin
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Fresh Fruit ◽  
Factors Affecting ◽  
Fresh Fruit Bunches ◽  
The Many

Zakat is one of the five points in rukun Islam and consists of two types: zakat nafs (soul) and zakat maal (wealth). One of the many kinds of zakat maal is plantage-product zakat. Labuhanbatu Selatan Regency is an area that contains 160,785.04 hectares of palm plantation land as recorded in 2016. The area also produced 7,493,696.18 tons of Fresh Fruit Bunches (FFB) in the same year. The objective of this study is to identify the potential of plantage-product zakat (specifically palm plantage) and analyze the factors that affect farmers’ decision to dispense plantage-product zakat in Labuhanbatu Selatan Regency. Logistic regression analysis was the method of analysis used in this study. Based on data obtained from Dinas Perkebunan Provinsi Sumatera Utara 2016, the potential of plantage-product zakat in Labuhanbatu Selatan Regency reached 25.6 billion rupiahs in 2014; 21.6 billion rupiahs in 2015, and 370.4 billion rupiahs in 2016. The variables that significantly affected farmers’ decisions to dispense plantage-product zakat were comprehension of zakat, faith, rewards, Islamic study, and frequency of worship.

scholarly journals Factors affecting internet use among university students in Sarawak, Malaysia: an empirical study

2020 ◽  
Vol 7 (3) ◽  
pp. 848
Author(s):  
M. Mizanur Rahman ◽  
M. Taha Arif ◽  
Fready Luke ◽  
Santha Letchumi ◽  
Fatin Nabila ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
University Students ◽  
Logistic Regression Analysis ◽  
Internet Use ◽  
Binary Logistic Regression ◽  
Problematic Internet Use ◽  
The Internet ◽  
Binary Logistic Regression Analysis ◽  
Factors Affecting

Background: The internet has become an indispensable tool for communication, academic research, information and entertainment. However, heavy users of the internet lead to less confidence in social skills and the tendency to be isolated. The study aimed to assess the pattern of internet use and factors affecting problematic internet use among university students.Methods: This cross-sectional study conducted among the students of a university in Sarawak, Malaysia. A multistage cluster sampling technique was adapted to select the participants. Data were collected from 463 students by self-administered questionnaire. Hierarchical binary logistic regression analysis was done to determine the potential factors for problematic internet use.Results: The mean age of the students was 22 years, with a standard deviation of 1.6 years. Two-fifths (61.8%) of the students had no problematic internet use. However, 35.4% had moderate and 2.8% had severe problematic internet use. Hierarchical binary logistic regression analysis found that age of the students, year of study, duration of daily internet use and use of social networking like Skype appeared to be potential predictors of problematic internet use (p<0.05).Conclusions: This study was conducted in only one university, thus did not depict the overall scenarios of the country. The implications of the findings are still worth noting in the process of designing internet addiction studies among university students. Overall, this study has unearthed some useful insights which can serve as a guide to more elaborate studies.

Analysis of cognitive characteristics and influencing factors of mass sports rehabilitation in Taiyuan

2019 ◽  
Author(s):  
Yaye He ◽  
Jiangong Wang ◽  
Liangyuan Zhao
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Health Status ◽  
Influencing Factors ◽  
Logistic Regression Analysis ◽  
Education Level ◽  
Factors Affecting ◽  
Exercise Rehabilitation ◽  
And Gender ◽  
Socio Economic Factors

Abstract Background: To assess the awareness regarding sports rehabilitation among residents of Taiyuan. Method: From September 27, 2018 to March 29, 2019, 1200 residents who met the inclusion/ criteria were selected using convenient sampling method. The population was surveyed by self-designed questionnaires, and single factor and two-category logistic regression analysis (stepwise forward method) was used to identify the factors influencing awareness of mass sports rehabilitation in Taiyuan. Results: A total of 1200 questionnaires were issued, of which 1167 were collected and 1101 were valid. The corresponding recovery and effective recovery rates were 97.25% and 94.34% respectively. The overall rate of awareness of exercise rehabilitation was 80.7%, and education level, occupation, income and health status were significant influencing factors (R<0.05). The results of two-class logistic regression analysis showed that age, occupation, education level, income level and health status were the influencing factors affecting the public's perception of the sports rehabilitation concept (R<0.05), whereas gender, occupation, education level and health status influenced understanding of the establishment of the rehabilitation department in Taiyuan (R<0.05), and gender, age, education level and health status affected understanding of the types of patients receiving rehabilitation (R<0.05). Conclusion: There is a high general awareness regarding sports rehabilitation, and is influenced by various socio-economic factors.

scholarly journals Investigation of Factors Affecting the Achievement of University Students with Logistic Regression Analysis: School of Physical Education and Sport Example

SAGE Open ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 215824402090208
Author(s):  
Yeliz Eratlı Şirin ◽  
Mustafa Şahin
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Academic Success ◽  
Physical Education ◽  
University Students ◽  
Logistic Regression Analysis ◽  
Factors Affecting ◽  
Course Success ◽  
Effective Factor ◽  
The Way

In this study, the factors affecting the success of university students were analyzed by logistic regression analysis. In the study, success variable was defined according to the survey information applied to 360 university students studying in School of Physical Education and Sport in Çukurova University and Kahramanmaraş Sütçü İmam University in Turkey, in 2017–2018 academic year. The relationship between the answers to the Likert-type scale questions affecting success variables and the course success was estimated by logistic regression analysis. According to the results of the research, because independent variables such as mother’s education status, age, and class were statistically insignificant, they were not included in the multivariate model. According to the findings, variables such as gender, the university they studied, the way they chose their department, and father’s education are seen as important in the growth of students’ academic success. In addition to this, the variables such as counseling about their profession, support of department’s instructors, and communication with instructor have been found to be considerably effective on success. It was observed that the way they chose their department (willingly–compulsorily) was the most effective factor, and father’s education was the second effective factor. As a result, the success levels of the students were found to differ according to the sociodemographic characteristics and their relations with the instructors. On the contrary, as the instructors’ guidance, support, and communication skills are effective contributors on student’s success, it has been concluded that instructors should take these factors into account.

scholarly journals Factors affecting the occurrence of proximal endoleak after endovascular abdominal aortic repair for abdominal aneurysms

2020 ◽  
Vol 48 (11) ◽  
pp. 030006052097151
Author(s):  
Daniela Mazzaccaro ◽  
Girolomina Mazzeo ◽  
Gianmarco Zuccon ◽  
Alfredo Modafferi ◽  
Giovanni Malacrida ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Aortic Aneurysms ◽  
P Value ◽  
Chi Square ◽  
Factors Affecting ◽  
Axial Diameter ◽  
Abdominal Aortic ◽  
Type Ia

Objective This retrospective study was performed to assess the clinical and radiological variables associated with proximal type IA endoleak (EL) in patients treated with elective endovascular repair for abdominal aortic aneurysms. Methods The chi-square test, t-test, and logistic regression analysis were performed as appropriate. A P value of <0.05 was considered statistically significant. Results The data of 79 patients were analyzed. No mortality occurred. During follow-up (median, 28.5 months; interquartile range, 12.8–43.0 months), 10 patients developed type IA EL. In the logistic regression analysis, undersizing of the endograft diameter by <10% significantly affected the occurrence of type IA EL. When the diameter was used for measurements, less oversizing was significantly associated with a higher risk of type IA EL. When the area was used for measurements, oversizing of >20% significantly affected the occurrence of type IA EL. Conclusion When sizing endografts, a discrepancy was noted between the measurements of the diameter and area of the proximal neck. The area might represent a more accurate measurement than the axial diameter to optimize the proximal sealing and lower the risk of developing type IA EL.

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