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...