Simple Linear Logistic Regression Research Articles

In-stent restenosis in acute coronary syndrome-a classic and a machine learning approach.

In acute coronary syndrome (ACS), a number of previous studies tried to identify the risk factors that are most likely to influence the rate of in-stent restenosis (ISR), but the contribution of these factors to ISR is not clearly defined. Thus, the need for a better way of identifying the independent predictors of ISR, which comes in the form of Machine Learning (ML). The aim of this study is to evaluate the relationship between ISR and risk factors associated with ACS and to develop and validate a nomogram to predict the probability of ISR through the use of ML in patients undergoing percutaneous coronary intervention (PCI). Consecutive patients presenting with ACS who were successfully treated with PCI and who had an angiographic follow-up after at least 3 months were included in the study. ISR risk factors considered into the study were demographic, clinical and peri-procedural angiographic lesion risk factors. We explored four ML techniques (Random Forest (RF), support vector machines (SVM), simple linear logistic regression (LLR) and deep neural network (DNN)) to predict the risk of ISR. Overall, 21 features were selected as input variables for the ML algorithms, including continuous, categorical and binary variables. The total cohort of subjects included 340 subjects, in which the incidence of ISR observed was 17.68% (n = 87). The most performant model in terms of ISR prediction out of the four explored was RF, with an area under the receiver operating characteristic (ROC) curve of 0.726. Across the predictors herein considered, only three predictors were statistically significant, precisely, the number of affected arteries (≥2), stent generation and diameter. ML models applied in patients after PCI can contribute to a better differentiation of the future risk of ISR.

An accurate soft diagnosis method of breast cancer using the operative fusion of derived features and classification approaches

AbstractBreast cancer is one of the most common types of cancer around the world. The early‐stage recognition of breast cancer is favourable for the diagnosis and treatment of the affecting patient. Data mining approaches can ease the diagnosis of breast cancer by analysis of the associated dataset for real‐time decision‐making. The present study proposes an effective transformation approach of experimental attributes of a breast cancer dataset using the latent semantic analysis and the fusion of derived features with classification methods for accurate recognition of breast cancer. The proposed approach is validated using the most widely used benchmark and open‐access breast cancer dataset. The transformed features of the original dataset result in 100% recognition efficiency using a multilayer perceptron, support vector machine, multi‐class classifier and functional tree classifiers. Other classifiers, like naïve Bayes, rotation forest, simple linear logistic regression and logistic model tree result in recognition accuracy between 96.85% and 99.30% using a similar feature subset. Besides, the optimal subset of derived features has been affirmed based on the evaluation metrics of classification approaches.

Why generation Y prefers online shopping: a study of young customers of India

The purpose of this research is to identify the relationship among online purchasing behaviour of the customer with customer's personal characteristics, demographic and webographic traits and the web-store qualities. This study employs logistic regression and artificial neural networks to predict customer's online purchase behaviour. A comparison has been made between the results obtained by logistic regression and artificial neural networks. The proposed methodology provides a better understanding of the buying behaviour of an online customer. The study uses simple linear logistic regression which may be extended further with nonlinear regression. For a neural network model to be robust enough to produce better results, more training data are required. A new approach has been described to predict the purchase behaviour of online customer based on logistic regression and artificial neural networks which may help the e-retailing sites to design the suitable strategies.

Prevalence of incidental findings on abdominal computed tomography angiograms on prospective renal donors.

To evaluate the prevalence of incidental findings on preoperative abdominal computed tomography angiography-computed tomography urography in asymptomatic prospective renal donors. A Health Insurance Portability and Accountability Act (HIPAA)-compliant, Institutional Review Board (IRB)-approved retrospective study of 1,597 subjects undergoing renal transplant evaluation from June 1, 2006, to March 31, 2011, was performed. Candidates underwent multiphasic multidetector computed tomography angiography-computed tomography urography for presurgical evaluation of renal vascular and parenchymal anatomy. All scans were reviewed by one of three fellowship-trained abdominal radiologists. The diagnoses were made on the basis of computed tomography characteristics of each lesion, and pathology confirmation was available for seven patients. We calculated the prevalence of each incidental finding, performed Fisher exact test or chi-square test for categorical variables between the cohort that did and did not undergo donor nephrectomy, and performed simple linear logistic regression analysis of incidental findings which predicted renal donation. Of the 1,597 potential donors, 58.4% were female, and the mean age was 42.6 years (range, 18-74). One thousand one hundred ninety-five (74.9%) had a total of 2,105 incidental findings. Based on American College of Radiology Incidental Findings Committee White Paper on Managing Incidental Findings on Abdominal Computed Tomography, 17.3% had incidentalomas and 1.1% required follow-up. Majority of the incidental findings (16 of 17) were in patients who did not undergo renal donation. The prevalence of pathologically proven malignancy was 0.1% (3 of 1,597). Preoperative computed tomography angiography-computed tomography urography not only identifies vascular anatomy but may also help detect clinically significant unanticipated findings in an otherwise healthy population.

Predictive modelling of the recovery of Listeria monocytogenes on sliced cooked ham after high pressure processing

This study examined bacterial recovery on sliced cooked ham that was inoculated with Listeria monocytogenes, treated by high pressure processing (HPP) and then stored at 10 °C for 70 days. The number of L. monocytogenes on the ham inoculated with 5 log 10 CFU/g was initially reduced by HPP at 500 MPa for 10 min to below the detectable level (10 CFU/g). However, the bacterial count gradually increased during storage, and exceeded the initial inoculum level at the end of the 70-day period, having risen by 7–8 log 10 CFU/g. A novel predictive model was therefore developed to estimate the recovery of L. monocytogenes during storage after HPP. Recovery of L. monocytogenes was defined as the detection of > 10 2 CFU/g bacteria, in view of the relevant food safety objectives of L. monocytogenes. At each 14-day sampling session, the ham was scored as either 1 or 0 indicating bacterial recovery or no bacterial recovery, respectively. The data were then subjected to a simple linear logistic regression model, which provided a good fit as indicated by the performance statistics. Using this model, we estimated the minimum HPP conditions necessary for the required storage periods. Additionally, as the developed model was based on logistic regression, the probability of the recovery of L. monocytogenes during storage after HPP was estimated. Our model not only calculated the appropriate shelf life and process conditions, but also provided a method for evaluating the risk of the recovery of pathogenic bacteria during storage.

LOTUS: An Algorithm for Building Accurate and Comprehensible Logistic Regression Trees

Logistic regression is a powerful technique for fitting models to data with a binary response variable, but the models are difficult to interpret if collinearity, nonlinearity, or interactions are present. Besides, it is hard to judge model adequacy because there are few diagnostics for choosing variable transformations and no true goodness-of-fit test. To overcome these problems, this article proposes to fit a piecewise (multiple or simple) linear logistic regression model by recursively partitioning the data and fitting a different logistic regression in each partition. This allows nonlinear features of the data to be modeled without requiring variable transformations. The binary tree that results from the partitioning process is pruned to minimize a cross-validation estimate of the predicted deviance. This obviates the need for a formal goodness-of-fit test. The resulting model is especially easy to interpret if a simple linear logistic regression is fitted to each partition, because the tree structure and the set of graphs of the fitted functions in the partitions comprise a complete visual description of the model. Trend-adjusted chi-square tests are used to control bias in variable selection at the intermediate nodes. This protects the integrity of inferences drawn from the tree structure. The method is compared with standard stepwise logistic regression on 30 real datasets, with several containing tens to hundreds of thousands of observations. Averaged across the datasets, the results show that the method reduces predicted mean deviance by 9% to 16%.We use an example from the Dutch insurance industry to demonstrate how the method can identify and produce an intelligible profile of prospective customers.

A computable confidence upper limit from discrete data with good coverage properties

We present a new and simple method for constructing a 1− α upper confidence limit for θ in the presence of a nuisance parameter vector ψ, when the data is discrete. Our method is based on computing a P-value P{ T⩽ t} from an estimator T of θ, replacing the nuisance parameter by the profile maximum likelihood estimate ψ ̂ (θ) for θ known, and equating to α. We provide a theoretical result which suggests that, from the point of view of coverage accuracy, this is close to the optimal replacement for the nuisance parameter. We also consider in detail limits for the (i) slope parameter of a simple linear logistic regression, (ii) odds ratio in two-way tables, (iii) ratio of means for two Poisson variables. In all these examples the coverage performance of our upper limit is a dramatic improvement on the coverage performance of the standard approximate upper limits considered.

Modification of the Minimum Logit Chi‐Squared Estimator in Simple Linear Logistic Regression

AbstractA new modification of Berkson's minimum logit chi‐squared estimator in simple linear logistic regression is suggested in order to achieve reduction of first order bias of the estimator as well as in the model. Furthermore, unlike estimators currently available, our procedure is quite simple to apply in practice and is valid even in the presence of zero frequencies in the table.

Modification of the Empirical Logit to Reduce Bias in Simple Linear Logistic Regression

Modification of the Empirical Logit to Reduce Bias in Simple Linear Logistic Regression

Modification of the empirical logit to reduce bias in simple linear logistic regression

The effect on the bias of Berkson's minimum logit chi-squared estimator of adding a constant to each observed count before forming the empirical logit is examined as a function of the number of design points. It is shown that the ‘best’ constant for reducing bias is essentially a linear function of the reciprocal of the number of design points.