Top Research Papers on Logistic Regression

The Logic of Logistic Regression Interpreting Logistic Regression Coefficients Estimation and Model Fit Probit Analysis Conclusion

Complete model reporting for binary logistic regression includes descriptive statistics, a statement on whether assumptions were checked and met, ORs and CIs for each predictor, overall model significance and overall model fit.

•A logistic regression model is used when the outcome of interest is binary. The term “logistic” refers to the underlying “logit” (log odds) function that is used to model the binary outcome. •Odds ratios are produced from a logistic regression model, and have a useful interpretation. •Tips, tricks and concepts used to fit logistic regression models are similar to those used in linear regression models. •Modeling building that is knowledge-based rather than automatic is preferred in most applications of logistic regression. •A logistic regression model that is overparameterized (ie too many va...

One of the most life-threatening disease is cardiovascular disease. Its high mortality rate contributes to nearly 17 million deaths all over the world. Early diagnosis helps to treat the disease in timely manner to prevent mortality. There are several machine and deep learning techniques available to classify the presence and absence of the disease. In this research, Logistic Regression (LR) techniques is applied to UCI dataset to classify the cardiac disease. To improve the performance of the model, pre-processing of data by Cleaning the dataset, finding the missing values are done and featur...

The data suggest that a diet high in vitamin B12, as well as alcohol use may be associated with self-reported breast cancer, and additional prospective studies should apply more recent statistical techniques to dietary data and cancer outcomes to replicate and confirm the present findings.

Background: Logistic regression is a classification model in machine learning, extensively used in clinical analysis. It uses probabilistic estimations which helps in understanding the relationship between the dependent variable and one or more independent variables. Diabetes, being one of the most common diseases around the world, when detected early, may prevent the progression of the disease and avoid other complications. In this work, we design a prediction model, that predicts whether a patient has diabetes, based on certain diagnostic measurements included in the dataset, and explore var...

LASSO regression showed a more efficient ability than Pearson chi-square test based logistic regression on differential diagnosing atypical CD and ITB in discriminating ITB and atypicals Crohn's disease.

In this proposed model, two machine-learning techniques were used for predicting customer churn Logistic regression and Logit Boost were used.

Logistic regression yields as good performance as ML models to predict the risk of major chronic diseases with low incidence and simple clinical predictors in a prospective cohort study in Asian adults.

The experimental conclusion shows that BBC news text classification model gets satisfying results on the basis of algorithms tested on the data set and is termed as the best machine learning algorithm for the BBC news data set.

Machine learning models were the less influenced by the dataset size but needed interaction terms to achieve good performance, whereas deep learning models could achieve good performance without interaction terms. Conclusively, with the considered scenarios, well-specified machine learning models outperformed deep learning models.

The sample size criteria was illustrated through a worked example considering the development of a multinomial risk prediction model for tumour type when presented with an ovarian mass and found that it resulted in the desired level of overfitting.

China's logistics industry has developed rapidly recently, but it also faces problems such as high costs, low efficiency and excessive carbon emissions, which has caused a heavy burden on the environment. However, there are few studies on the consideration of carbon emission factors in logistics performance evaluation. To this end, this study developed a comprehensive evaluation index system to assess the performance of China's logistics. Principal Component Analysis (PCA) was applied to reduce the indicator dimensions and then a Slacks-Based Measure-Data Envelopment Analysis (SBM-DEA) was emp...

Breast cancer is the most common cancer among women such that the existence of a precise and reliable system for the diagnosis of benign or malignant tumors is critical. Nowadays, using the results of Fine Needle Aspiration (FNA) cytology and machine learning techniques, detection and early diagnosis of this cancer can be done with greater accuracy. In this paper, we propose a method consisting of two steps: in the first step, to eliminate the less important features, logistic regression has been used. In the second step, the Group Method Data Handling (GMDH) neural network is used for the dia...

Financial risk tolerance is one of the important factors affecting the financial investment decisions of individuals and institutional investors and a crucial factor of financial planning and financial counseling. It is therefore necessary to determine the major determinants of risk tolerance. In this article, we researched the impact of financial literacy level and demographic characteristics on the financial risk tolerance of the individuals in the sample of Usak University staff, using a multinomial logistic regression analysis and retrieving data through the questionnaire method. Multinomi...

An efficient multiclass objects categorization method is proposed for the indoor-outdoor scene classification of scenery images using benchmark datasets and Experimental evaluation demonstrated that the scene classification method is superior compared to other conventional methods, especially when dealing with complex images.

A novel spam detection method that combines the artificial bee colony algorithm with a logistic regression classification model is proposed that outperforms other spam detection techniques considered in this study in terms of classification accuracy.

Forest fire risk has increased globally during the previous decades. The Mediterranean region is traditionally the most at risk in Europe, but continental countries like Serbia have experienced significant economic and ecological losses due to forest fires. To prevent damage to forests and infrastructure, alongside other societal losses, it is necessary to create an effective protection system against fire, which minimizes the harmful effects. Forest fire probability mapping, as one of the basic tools in risk management, allows the allocation of resources for fire suppression, within a fire se...

Outcome imbalance is not a problem in itself, imbalance correction may even worsen model performance, and inaccurate probability estimates reduce the clinical utility of the model, because decisions about treatment are ill-informed.

When the outcome is binary, psychologists often use nonlinear modeling strategies such as logit or probit. These strategies are often neither optimal nor justified when the objective is to estimate causal effects of experimental treatments. Researchers need to take extra steps to convert logit and probit coefficients into interpretable quantities, and when they do, these quantities often remain difficult to understand. Odds ratios, for instance, are described as obscure in many textbooks (e.g., Gelman & Hill, 2006, p. 83). I draw on econometric theory and established statistical findings to de...

A high-performance and interpretable credit scoring method called penalised logistic tree regression (PLTR), which uses information from decision trees to improve the performance of logistic regression.

A parameter is introduced that measures the goodness of fit of a model but does not depend on the sample size, which is a step‐by‐step illustration of the method using a model for postneonatal mortality developed in a large cohort of more than 300 000 observations.

This study used four machine-learning methods, namely Kernel Logistic Regression, Radial Basis Function Classifier, Multinomial Naïve Bayes, and Logistic Model Tree, to generate flash flood susceptibility maps at the minor part of Nghe An province of the Center region (Vietnam) where recurrent flood problems are being experienced.

Abstract We are experiencing a serious health crisis due to COVID‐19 that has a major impact on the field of education. The educational system therefore needs to be updated and innovated, with the addition of digital resources, to adapt the teaching and learning processes to students with disabilities. To meet the goal of high‐quality education, teachers must have adequate digital competence to face the educational demands that are placed on them. Therefore, the purposes of this study are: to know the teachers' knowledge about digital resources to support students with disabilities (O1); at ea...

Development on Rinca Island by the Indonesian Government has received a lot of reaction from the community. Masses expressed their opinion through social media, especially Twitter regarding the matter. The research was conducted to analyze the public's sentiment about this development which was divided into three categories: pro, contra, and neutral. There are two Doc2Vec models used in this research, the distributed model, and the distributed bag of words, and using support vector machines and logistic regression as classifiers. Each combination of the models and classifier has an accuracy ra...

A computational method that can efficiently identify BBPs using logistic regression, BBPpred (blood-brain barrier peptides prediction), is described, which investigates a wide variety of features from amino acid sequence information, and then a feature learning method is adopted to represent the informative features.

The findings make up for the crucial step in LSM (hyperparameter optimization) through the Bayesian algorithm, and provide a comparison case between LR and RF models after comprehensive consideration of hyperparameter optimize, so as to increase the convincing power of the comparison of these models.

The binary logistic regression concludes that respondents exhibiting high‐perceived threat, less product involvement, less perceived benefit on OFDs and less frequency of online food orders are less likely to order food through OFDs.

In order to reduce risks of failure, industries use a methodology called Failure Mode and Effects Analysis (FMEA) in terms of the Risk Priority Number (RPN). The RPN number is a product of ordinal scale variables, severity (S), occurrence (O) and detection (D) and product of such ordinal variables is debatable. The three risk attributes (S, O, and D) are generally given equal weightage, but this assumption may not be suitable for real-world applications. Apart from severity, occurrence, and detection, the presence of other risk attributes may also influence the risk of failure and hence should...

Assessment of machine learning models at predicting acute kidney injury (AKI) suggests that ML models perform equally to that of LR, however ML models exhibit variable performance with some ML models displaying exceptional performance.

It is recommended that people living in high-risk environments, such as those living in houses of multiple occupancy, should be specially supported when asked to stay at home, and public health messaging should emphasise shared responsibility and public consciousness.

Goncha Siso Eneses area of East Gojam Zone in northwestern Ethiopia is one of the most landslide-prone regions, which is characterized by frequent landslide occurrences causing fatalities and damages in cultivated and non-cultivated lands, infrastructure and properties. Hence, preparing a landslide susceptibility map is very helpful in reducing the damages in infrastructure and properties and loss of animal and human lives. In this study, GIS-based information value and logistic regression models were applied. A reliable and detailed landslide inventory with 894 landslides was prepared through...

It is confirmed that GBDT provides a more reliable model than that of LR in the development of diabetes prediction models using big data, which could potentially produce a highly reliable diabetes prediction model, a helpful tool for improving lifestyle and preventing diabetes.

Main findings suggest that ML accurately predicts ROA and ROE and indicate, through the ordered logistic regression model, the existence of a positive relationship between ESG practices and the financial indicators.

Clarity is provided around fixed effects models in TSCS data and a novel technique to identify which one to use as a function of the frequency of events in y is provided.

Background Predictions in pregnancy care are complex because of interactions among multiple factors. Hence, pregnancy outcomes are not easily predicted by a single predictor using only one algorithm or modeling method. Objective This study aims to review and compare the predictive performances between logistic regression (LR) and other machine learning algorithms for developing or validating a multivariable prognostic prediction model for pregnancy care to inform clinicians’ decision making. Methods Research articles from MEDLINE, Scopus, Web of Science, and Google Scholar were reviewed follow...

The constraints inequalities in the constraints of e-insensitive SVR are changed to fuzzy inequalities without any changes in its loss function, which gives more flexibility to the SVR constraints for satisfaction.

Severity levels of anemia among children aged 6–59 months in sub-Saharan Africa was a major public health problem and enhancing maternal education, providing drugs for an intestinal parasite, designing interventions that address maternal anemia, febrile illness, and diarrheal disease, and strengthening the economic status of the family are recommended to reduce childhood anemia.

Forest canopy density (FCD) is a useful measure to assess the forest cover change in its own as numerous works of forest change have been done using only FCD with the help of remote sensing and GIS.

This study focuses on the transformation of logistics centers in Industry 4.0. Aim is to reveal the important criteria for logistics centers in Industry 4.0 by considering link to traditional logistics centers practices and proposing a framework for new logistics centers. Initially, literature review to reveal criteria for logistics centers in Industry 4.0 is conducted. Secondly, fuzzy multi-criteria decision-making methodology is used to present the importance order, and the causal relationship between criteria to make recommendations for future implications. The results may be useful for log...