Top Research Papers on Logistic Regression

KEY POINT: Logistic regression is used to estimate the relationship between one or more independent variables and a binary (dichotomous) outcome variable.

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.

This study proposed the Logistic Regression (LR) method to classify HCC data and found that LR-GA is a better machine learning method than LR with 93.18%, 90.91 %, 95.45%, and 93.12% values for accuracy, recall, precision, and f1-score respectively.

Standard logistic regression handles binary outcomes such as disease/no disease. Researchers sometimes collapse outcomes with more than two groups into binary variables to enable use of this familiar model. However, this practice risks losing information and statistical power. In fact, logistic regression is not limited to binary outcomes: Multinomial logistic regression can handle outcomes with more than two groups; and ordinal logistic regression can handle outcomes with more than two ordered groups. This article aims to demystify these two models.

The first data oblivious sketch for logistic regression is presented, which can be computed in input sparsity time over a turnstile data stream and reduces the size of a $d-dimensional data set from $n$ to only $\operatorname{poly}(\mu d\log n)$ weighted points, where $\mu$ is a useful parameter which captures the complexity of compressing the data.

A novel construction that enables two parties to securely train a logistic regression model on private secret-shared data and develops many building blocks of independent interest, including a new ap-proximation technique for the sigmoid function that results in a secure protocol with better communication.

This article explains how to perform a logistic regression using practical examples, and the assumptions that need to be checked are explained, along with the process of checking them using the R software.

A unified framework to solve $\ell_{1}$-regularized logistic regression, which can be naturally extended to nonconvex regularization term, as long as certain requirement is satisfied is proposed.

Predicting dichotomous outcomes is central to epidemiologic science and clinical care and is used to describe and test the relationship between a dichotomyous outcome and one or more potentially predictive variables.

Using financial ratio data from 2006 and 2007, this study uses a three-fold cross validation scheme to compare the classification and pre- diction of bankrupt firms by robust logistic regression with the Bianco and Yohai (BY) estimator versus maximum likelihood (ML) logistic regression. With both the 2006 and 2007 data, BY robust logistic regression improves both the classification of bankrupt firms in the training set and the prediction of bankrupt firms in the testing set. In an out of sample test, the BY robust logistic regression correctly predicts bankruptcy for Lehman Brothers; however, ...

Linear regression modeling is well suited to predicting continuous data where the outcome y is a real number (i.e., y ∈ ℝ). Logistic regression is a modeling technique for binary outcomes (i.e., yes/no, true/false, 1/0). Such outcomes are needed in many domains: public health officials might want to know the likelihood that a person will contract COVID-19 if she is a doctor in Ontario;a hospital would like to know if a discharged patient is more likely to be readmitted or not;a company would like to know if a customer visiting its website is more likely to order;a bank would like to know if a ...

Linear regression modeling is well suited to predicting continuous data where the outcome y is a real number (i.e., y ∈ ℝ). Logistic regression is a modeling technique for binary outcomes (i.e., yes/no, true/false, 1/0). Such outcomes are needed in many domains: public health officials might want to know the likelihood that a person will contract COVID-19 if she is a doctor in Ontario;a hospital would like to know if a discharged patient is more likely to be readmitted or not;a company would like to know if a customer visiting its website is more likely to order;a bank would like to know if a ...

Applied Logistic Regression, Third Edition provides an easily accessible introduction to the logistic regression model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables.

The purpose of this investigation was to illustrate, using a substantive example, the potential increase in power gained from an ordinal instead of a dichotomous specification for an inherently continuous response.

Today, there are not many good measures for detecting influential observations in case of fitting a logistic regression model. So, the purpose of this article is to extrapolate from the pre-existing deletion diagnostics defined for detecting influential points for multiple linear regression, i.e. the DFFITS, DFBETAS and Cook's Distance to the scenario of a binary logistic regression model and then view the multinomial model as a special case of the same. The threshold for determining whether an observation is an influential observation or not is judged using the asymptotic distribution of the ...

This review focused on logistic regression model, a kind of predictive modeling used in machine learning, and how healthcare researchers take decisions by the help of predictive modeled.

Although logistic regression models are widely used in multivariable analyses with dichotomous outcomes, many of their features, which can be very helpful tools in better understanding the data, are often underutilized. In addition to the widely used and reported odds ratios and p-values, PROC LOGISTIC generates a plethora of statistics from which one can gain further insight, make stronger analytical inferences, and more easily identify errors in the model construction. Model options will be explored, and resulting outputs from real-world examples will be explained in detail. Topics covered i...

This technical note presents the reason for using a binomial logic regression in marketing applications and a consumer-utility-based behavioral rationale is presented for the applicability of the binomial logistic regression for modeling dummy variables.

The authors' bounds are tight up to constant factors and the forward error bounds can be extended to Generalized Linear Models.

Like contingency table analyses and 2 tests, logistic regression allows the analysis of dichotomous or binary outcomes with 2 mutually exclusive levels.1 However, logistic regression permits the use of continuous or categorical predictors and provides the ability to adjust for multiple predictors. This makes logistic regression especially useful for analysis of observational data when adjustment is needed to reduce the potential bias resulting from differences in the groups being compared.2 Use of standard linear regression for a 2-level outcome can produce very unsatisfactory results. Predict...

The main advantage is that the output of the prediction equation is a probability on a proper 0-1 scale, which means that the authors have some direct and natural measure of the uncertainty in the assignment.

Logistic regression has probably been underutilized in clinical investigations of personality because of its relatively recent development (dictated by the need for computer programs to obtain maximum likelihood estimates), and the fact that use has been largely confined to the fields of biostatistics, epidemiology, and economics Its use should be given serious consideration when the outcome of interest is dichotomous (or polychotomous) in nature and the predictors of interest may be categorical or continuous. The logit transformation is quite tractable mathematically, and it embodies the noti...

It was commented that reoperation was nearly twice as likely when the tumour had a carcinoma in situ component recorded, and the association between breast reoperation and patients’ characteristics was examined.

1 Many statistical tests require the dependent (response) variable to be continuous so a different set of tests are needed when the dependent variable is categorical. One of the most commonly used tests for categorical variables is the Chi-squared test which looks at whether or not there is a relationship between two categorical variables but this doesn’t make an allowance for the potential influence of other explanatory variables on that relationship. For continuous outcome variables, Multiple regression can be used for

Like contingency table analyses and 2 tests, logistic regression allows the analysis of dichotomous or binary outcomes with 2 mutually exclusive levels.1 However, logistic regression permits the use of continuous or categorical predictors and provides the ability to adjust for multiple predictors. This makes logistic regression especially useful for analysis of observational data when adjustment is needed to reduce the potential bias resulting from differences in the groups being compared.2 Use of standard linear regression for a 2-level outcome can produce very unsatisfactory results. Predict...

This paper aims to summarize some articles from three regression model including logistic regression, linear regression, and non-linear regression used in predicting lung cancer, analyzing the drug efficacy of lung cancer related drugs, and so on.

The research results show that the MLR is the best model for modeling IPKM in Java based on the AIC value criteria and the factors that have a significant influence on public health development are the egg and milk consumption level and the percentage of the number of doctors per thousand population.

A differentially private method for sparse logistic regression that maintains hard zeros is presented, to first train a non-private LASSO logistics regression model to determine an appropriate privatized number of non-zero coefficients to use in final model selection.

This study suggests a rule-based model to evaluate the accuracy of applying rules to the individual findings of logistic regression, decision trees, and support vector machines on the Cleveland Heart Disease Database.

Factors that affect the recovery time of Covid-19 sufferers in West Sumatra showed that there was no difference between the logistic regression model and the GWLR models based on the values of AIC and the ratio of deviance and degrees of freedom (df).

A new regression method called Kappa regression is introduced to model conditional probabilities and is based on Dombi’s Kappa function, which is well known in statistics.

In many ways, binomial logistic regression is similar to linear regression, with the exception of the measurement type of the dependent variable (i.e., linear regression uses a continuous dependent variable rather than a dichotomous one). However, unlike linear regression, you are not attempting to determine the predicted value of the dependent variable, but the probability of being in a particular category of the dependent variable given the independent variables. An observation is assigned to whichever category is predicted as most likely. As with other types of regression, binomial logistic...

This paper shows that distributionally robust logistic regression with mixed features, despite amounting to an optimization problem of exponential size, admits a polynomial-time solution scheme, and develops a practically efficient column-and-constraint approach that solves the problem as a sequence of polynometric-time solvable exponential conic programs.

A negative result is shown, namely, that no strongly sublinear sized coresets exist for logistic regression, and to deal with intractable worst-case instances, a complexity measure $\mu(X)$ is introduced, which quantifies the hardness of compressing a data set for Logistic regression.

The research results confirm that the application of the logistic regression algorithm for solving loan classification problems is appropriate and allows to get quickly a working loan  classification tool.

The aim of present paper is to analyze recent trends in the detection of network attacks targeting Internet of Things devices as well as to propose a combined detector based on logistic regression and Emotional Model used to detect compromised IoT devices.

The risks of two kinds of project management are compared and the goal of this research is to model the project success in case of different types of projects and project management approaches.

Multiple Logistic Regression Just as in OLS regression, logistic models can include more than one predictor. The analysis options are similar to regression. One can choose to select variables, as with a stepwise procedure, or one can enter the predictors simultaneously, or they can be entered in blocks. Variations of the likelihood ratio test can be conducted in which the chi-square test (G) is computed for any two models that are nested. Nested models are models in which only a subset of predictors from the full model are included. A chi-square test is not valid unless the two models compared...

This article presents a parallel solution for computing logistic regression based on distributed asynchronous task framework that does not rely on any third-party coordinator, and hence has better security and can solve the multitraining problem.

A machine learning (ML)-based approach is proposed to identify malicious users from URL data using Logistic Regression and the results highlight positive steps forward of the proposed approach.

This work fills in this research gap by proposing a vertical federated unlearning method on logistic regression model that has a negligible decrease in accuracy but can improve training efficiency by over 400%.

A differentially private method for sparse logistic regression that maintains hard zeros is presented, to first train a non-private LASSO logistics regression model to determine an appropriate privatized number of non-zero coefficients to use in final model selection.

The goal is to evaluate precisely the (maximal) minimax regret which is analyzed using a unique and novel combination of information-theoretic and analytic combinatorics tools such as Fourier transform, saddle point method, and Mellin transform in the multi-dimensional settings.

This study determines the computational cost of logistic regression, using theoretical and experimental approaches, and provides a rigorous statistical answer to the crucial question that torments us, namely where does this logistic function on which most neural network algorithms are based come from?

The experience with competition from the perspective of educational data mining, a field founded in the learning sciences and connected with roots in psychology and statistics, and how learner model predictions are used to make pedagogical decisions for students are described.

Simple and multiple linear regression models study the relationship between a single continuous dependent variable Y and one or multiple independent variables X, respectively, using the logistic model and the probit model.

 In tennis, momentum is pivotal and can be quantified using metrics like Consecutive Win Rate (CWR), Unforced Error Rate (UER), Break Point Save Rate (BPSR), and Fatigue Factor (FF). Each metric provides insight into a player's performance and state during a match. CWR is a clear momentum indicator, reflecting a player's game dominance, while UER highlights potential lapses in concentration or physical condition. BPSR evaluates a player's clutch performance in critical situations, and FF gauges physical exertion. Utilizing logistic regression, we can predict a player's probability to win at an...

This work proposes to improve the performance of the Logistic Regression (LR) which is the most used ML model in radiomics, and proposes to represent the radiomics features with one-hot encoding, showing that the resulting LR is non-linear and almost equivalent to the Naive Bayes classifier (NBC).

The maximal minimax regret is introduced that is a general lower bound for the pointwise regret of a general logistic regression over all algorithms (learning distributions) and extended to loss functions other than logistic loss and non-binary labels.

Abstract Purpose The purpose of this study is to develop and compare model choice strategies in context of logistic regression. Model choice means the choice of the covariates to be included in the model. Design/methodology/approach The study is based on Monte Carlo simulations. The methods are compared in terms of three measures of accuracy: specificity and two kinds of sensitivity. A loss function combining sensitivity and specificity is introduced and used for a final comparison. Findings The choice of method depends on how much the users emphasize sensitivity against specificity. It also d...