scholarly journals Characterizing the Spatial Distribution of Eragrostis Curvula (Weeping Lovegrass) in New Jersey (United States of America) Using Logistic Regression

Environments ◽  
2019 ◽  
Vol 6 (12) ◽  
pp. 125
Author(s):  
Kikombo Ilunga Ngoy ◽  
Daniela Shebitz
Keyword(s):  
Invasive Species ◽  
Logistic Regression ◽  
New Jersey ◽  
Regression Model ◽  
Goodness Of Fit ◽  
Logistic Regression Model ◽  
Invasive Plant ◽  
Predictive Accuracy ◽  
Global Environmental Change ◽  
Eragrostis Curvula

The increasing spread of invasive plants has become a critical driver of global environmental change. Once established, invasive species are often impossible to eradicate. Therefore, predicting the spread has become a key element in fighting invasive species. In this study, we examined the efficiency of a logistic regression model as a tool to identify the spatial occurrence of an invasive plant species. We used Eragrostis curvula (Weeping Lovegrass) as the dependent variable. The independent variables included temperature, precipitation, soil types, and the road network. We randomly selected 68 georeferenced points to test the goodness of fit of the logistic regression model to predict the presence of E. curvula. We validated the model by selecting an additional 68 random points. Results showed that the probability to successfully predict the presence of E. Curvula was 82.35%. The overall predictive accuracy of the model for the presence or absence of E. Curvula was 80.88%. Additional tests including the Chi-square test, the Hosmer–Lemeshow (HL) test, and the area under the curve (AUC) values, all indicated that the model was the best fit. Our results showed that E. curvula was associated with the identified variables. This study suggests that the logistic regression model can be a useful tool in the identification of invasive species in New Jersey.

Relevance of Brain Regions’ Eloquence Assessment in Patients With a Large Ischemic Core Treated With Mechanical Thrombectomy

Neurology ◽  
2021 ◽  
pp. 10.1212/WNL.0000000000012863
Author(s):  
Basile Kerleroux ◽  
Joseph Benzakoun ◽  
Kévin Janot ◽  
Cyril Dargazanli ◽  
Dimitri Daly Eraya ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Random Forest ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Mechanical Thrombectomy ◽  
Predictive Accuracy ◽  
Unmet Need ◽  
C Statistic ◽  
Ischemic Core ◽  
Functional Relevance

ObjectiveIndividualized patient selection for mechanical thrombectomy (MT) in patients with acute ischemic stroke (AIS) and large ischemic core (LIC) at baseline is an unmet need.We tested the hypothesis, that assessing the functional relevance of both the infarcted and hypo-perfused brain tissue, would improve the selection framework of patients with LIC for MT.MethodsMulticenter, retrospective, study of adult with LIC (ischemic core volume > 70ml on MR-DWI), with MRI perfusion, treated with MT or best medical management (BMM).Primary outcome was 3-month modified-Rankin-Scale (mRS), favourable if 0-3. Global and regional-eloquence-based core-perfusion mismatch ratios were derived. The predictive accuracy for clinical outcome of eloquent regions involvement was compared in multivariable and bootstrap-random-forest models.ResultsA total of 138 patients with baseline LIC were included (MT n=96 or BMM n=42; mean age±SD, 72.4±14.4years; 34.1% females; mRS=0-3: 45.1%). Mean core and critically-hypo-perfused volume were 100.4ml±36.3ml and 157.6±56.2ml respectively and did not differ between groups. Models considering the functional relevance of the infarct location showed a better accuracy for the prediction of mRS=0-3 with a c-Statistic of 0.76 and 0.83 for logistic regression model and bootstrap-random-forest testing sets respectively. In these models, the interaction between treatment effect of MT and the mismatch was significant (p=0.04). In comparison in the logistic regression model disregarding functional eloquence the c-Statistic was 0.67 and the interaction between MT and the mismatch was insignificant.ConclusionConsidering functional eloquence of hypo-perfused tissue in patients with a large infarct core at baseline allows for a more precise estimation of treatment expected benefit.

Data Mining Medical Information

2011 ◽  
pp. 92-107
Author(s):  
Thomas Chesney ◽  
Kay Penny ◽  
Peter Oakley ◽  
Simon Davies ◽  
David Chesney ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Medical Information ◽  
Predictive Accuracy ◽  
Outcome Analysis ◽  
Audit Data ◽  
Patient Will ◽  
Artificial Neural Network Ann ◽  
Dissemination Of Results

Trauma audit is intended to develop effective care for injured patients through process and outcome analysis, and dissemination of results. The system records injury details such as the patient’s sex and age, the mechanism of the injury, various measures of the severity of the injury, initial management and subsequent management interventions, and the outcome of the treatment including whether the patient lived or died. Ten years’ worth of trauma audit data from one hospital are modelled as an Artificial Neural Network (ANN) in order to compare the results with a more traditional logistic regression analysis. The output was set to be the probability that a patient will die. The ANN models and the logistic regression model achieve roughly the same predictive accuracy, although the ANNs are more difficult to interpret than the logistic regression model, and neither logistic regression nor the ANNs are particularly good at predicting death. For these reasons, ANNs are not seen as an appropriate tool to analyse trauma audit data. Results do suggest, however, the usefulness of using both traditional and non-traditional analysis techniques together and of including as many factors in the analysis as possible.

Data Mining Medical Information

2011 ◽  
pp. 2218-2231
Author(s):  
Thomas Chesney ◽  
Kay Penny ◽  
Peter Oakley ◽  
Simon Davies ◽  
David Chesney ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Medical Information ◽  
Predictive Accuracy ◽  
Outcome Analysis ◽  
Audit Data ◽  
Patient Will ◽  
Artificial Neural Network Ann ◽  
Dissemination Of Results

Trauma audit is intended to develop effective care for injured patients through process and outcome analysis, and dissemination of results. The system records injury details such as the patient’s sex and age, the mechanism of the injury, various measures of the severity of the injury, initial management and subsequent management interventions, and the outcome of the treatment including whether the patient lived or died. Ten years’ worth of trauma audit data from one hospital are modelled as an Artificial Neural Network (ANN) in order to compare the results with a more traditional logistic regression analysis. The output was set to be the probability that a patient will die. The ANN models and the logistic regression model achieve roughly the same predictive accuracy, although the ANNs are more difficult to interpret than the logistic regression model, and neither logistic regression nor the ANNs are particularly good at predicting death. For these reasons, ANNs are not seen as an appropriate tool to analyse trauma audit data. Results do suggest, however, the usefulness of using both traditional and non-traditional analysis techniques together and of including as many factors in the analysis as possible.

Intuitionistic Fuzzy Partial Logistic Regression Model Using Ridge Methodology

2020 ◽  
Vol 28 (04) ◽  
pp. 527-543
Author(s):  
Gholamreza Hesamian ◽  
Mohammad Ghasem Akbari ◽  
Mehdi Roozbeh
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Smooth Function ◽  
Potential Application ◽  
Goodness Of Fit ◽  
Logistic Regression Model ◽  
Numerical Examples ◽  
Ridge Estimation ◽  
Intuitionistic Fuzzy

This paper applies a ridge estimation approach in an existing partial logistic regression model with exact predictors, intuitionistic fuzzy responses, intuitionistic fuzzy coefficients and intuitionistic fuzzy smooth function to improve an existing intuitionistic fuzzy partial logistic regression model in the presence of multicollinearity. For this purpose, ridge methodology is also involved to estimate the parametric intuitionistic fuzzy coefficients and nonparametric intuitionistic fuzzy smooth function. Some common goodness-of-fit criteria are also used to examine the performance of the proposed regression model. The potential application of the proposed method are illustrated and compared with the intuitionistic partial logistic regression model through two numerical examples. The results clearly indicate the proposed ridge method is quite efficient in model’s performances when there is multicollinearity among the predictors.

scholarly journals Handicraft small enterprises as an instrument for rural economic growth and poverty eradication

2020 ◽  
Vol 18 (4) ◽  
pp. 25-36
Author(s):  
Oluwayemisi A. Abisuga-Oyekunle ◽  
Mammo Muchie
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Goodness Of Fit ◽  
Logistic Regression Model ◽  
Growth Potential ◽  
Small Enterprises ◽  
Potential Contribution ◽  
Goodness Of Fit Test ◽  
Kwazulu Natal ◽  
Two Factors

In South Africa, exploiting economic opportunities in the handicraft sector could create livelihood and employment for ordinary citizens living in rural areas. The potential contribution of handicraft small enterprises to sustainable livelihoods and poverty alleviation is yet to be fully exploited. It is also regarded as a sector with great growth potential, but the degree of support provided to the handicraft sector is low. The study aims to evaluate the socioeconomic factors influencing the viability of handicraft small businesses operating in KwaZulu-Natal. Data collection was drawn from a stratified random sample of 196 handicraft practitioners operating in different areas of KwaZulu-Natal Province with a structured questionnaire. Data analysis was performed with the STATA statistical package. The results obtained from the study have shown that 84 enterprises (42.86%) were not viable, whereas 112 of the 196 handicraft enterprises (57.14%) were viable. The percentage of overall correct classification for this procedure was equal to 77.96%. Percentage sensitivity for the fitted logistic regression model was equal to 60.71%. Percentage specificity for the fitted logistic regression model was equal to 82.14%. The p-value obtained from Hosmer-Lemeshow goodness-of-fit test was equal to 0.0884 > 0.05. This indicates that the fitted logistic regression model is fairly well reliable. The findings from the analysis showed that two factors significantly influenced the viability of handicraft enterprises. These two factors were the belief that handicraft business could sustain the handicraft practitioner, and the level of support for handicraft businesses from non-governmental organizations is decreasing. AcknowledgmentSouth Africa SarChi Chair, Nation Research Fund and Department of Science and Technology, South African, for providing funding for this research.

A Predictive Statistical Model To Identify Thalassemic Phenotype (β0 vs β+) in Heterozygous β-Thalassemia.

Blood ◽  
2007 ◽  
Vol 110 (11) ◽  
pp. 3823-3823
Author(s):  
Jose Manuel Calvo-Villas ◽  
Paloma Ropero ◽  
Silvia De la Iglesia ◽  
Maria Francisca Zapata ◽  
Alejandro Mayosky ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Goodness Of Fit ◽  
Logistic Regression Model ◽  
Globin Gene ◽  
Genetic Alterations ◽  
Theoretic Model ◽  
Fisher Exact Test ◽  
Globin Genes ◽  
Adjusted Model

Abstract β thalassemias are a heterogeneous group of genetic alterations characterized by a decreasing (β+) or eliminating (β0) expression of b globin gene. Previous studies have reported that mean corpuscular volume (MCV) and mean corpuscular haemoglobin (MCH) values would be useful to differentiate between β+ and β0 thalassemia phenotypes in heterozygous carriers. An accurate and valid risk model for belonging to each of these groups may be valuable for prioritizing identification of genetic alterations in some populations. The aim of this study was to develop a theoretic model that combine information from the haematological indices for phenotypic prediction (β+ vs. β0) in heterozygous β thalassemia. The study was conducted on 238 unrelated β thalassemia carriers. Hematological parameters were obtained using analyzer Coulter ®GEN-S™. HbA2 and HbF were analysed by high performance liquid chromatography. Molecular analysis for β globin and α-globin genes was performed by real-time PCR, direct sequencing techniques and standard PCR techniques. β-thalassemic subjects who also exhibited either αthalassemia or extra α globin genes were excluded. Statistical significance was calculated by using the χ2 or Fisher exact test for qualitative variables and the t test for continuous variables. A multivariate logistic regression model, with stepwise selection, was fitted from a case group of 64 β+thalassemia subjects and a reference group of 174 β0carriers. Odds ratios and their 95% confidence intervals (CIs) were computed. Statistical analysis was performed with the SPSS 12.0 statistical software package. The mean level of HbA2 (4.5 ± 0.7% vs. 5.1 ± 0.7%, [p<0.0001]), MCV (66.9 ± 4.6 fL vs. 62.9 ± 3.1 fL, [p<0.0001]) and MCH (21.6 ± 1.8 pg vs. 20.1 ± 1.1 pg, [p<0.0001]) were significantly different between the β+ and β0 groups. By using an multivariate analysis, the three significant variables that finally entered the logistic regression model were MCH, HbA2 and HbF. These variables were dichotomized as HbA2> 4 vs. HbA2< 4, MCH> 20.5 vs. MCH< 20.5 and HbF> 1.5 vs. HbF< 1.5. The adjusted model implies that with a HbA2< 4, the chance of having β+thalassemia increase by a factor 8.3 (CI 1.6–43.1) compared with a HbA2> 4. Analogously, at a MCH > 20.5, the chance of having β +thalassemia increase by a factor 9.4 (CI 4.3–20.2) compared with a MCH< 20.5. Regarding to HbF, it is 1.3 times more likely (CI 1.1–1.9) to be included in the β+thalassemia group with HbF< 1.5 than with HbF> 1.5. For this adjusted model, with a 26,8% heterozygous β+thalassemia prevalence, model test performance characteristics included: sensitivity 61.4% and specificity 89.1% (p=0.000). The logistic multivariate model to allow accurate prediction of the phenotype of β thalassemia trait (β+ vs β0) in the 84.1% of the carriers. The Hosmer-Lemeshow statistics indicated fine goodness of fit of the logistic regression equation (P=0.739). In summary, this logistic regression analysis using a combination of hematological indices such as MCH, HbA2 and HbF has showed an acceptable value in predicting phenotype of heterozygous b thalassemia (β+ vs β0) in a predominant Spanish population. The present model should be validated by independent data from different populations.

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