PURPOSE To develop and evaluate the clinical applicability of advanced machine learning models that simultaneously predict multiple optimization objective function weights from patient geometry for intensity-modulated radiation therapy of prostate cancer. METHODS A previously developed inverse optimization method was applied retrospectively to determine optimal objective function weights for ...

the aim of this study was to investigate the potential association between growth hormone gh/alui and growth hormone receptor ghr/alui polymorphisms with milk yield and reproductive performances in holstein dairy cows in iran. blood samples of 150 holstein cows were collected and their genomic dna was extracted using gene-fanavaran dna extracting kit. fragments of the 428 bp of exon 5 growth ho...

The two-group cross-validation classification accuracies of six algorithms (i.e., least squares, ridge regression, principal components, a common factor method, equal weighting, and logistic regression) were compared as a function of degree of validity concentration, group separation, and number of subjects. Therein, the findings of two previous studies were extended to the latter three methods...

BACKGROUND Using peak expiratory flow (PEF) as an alternative to spirometry parameters (FEV1 and FVC), for detection of airway reversibility in diseases with airflow limitation is challenging. We developed logistic regression (LR) model to discriminate bronchodilator responsiveness (BDR) and then compared the results of models with a performance of >18%, >20%, and >22% increase in ΔPEF% (PEF ch...

This paper proposes a novel ensemble learning approach based on logistic regression (LR) and artificial intelligence tool, i.e. support vector machine (SVM) and back-propagation neural networks (BPNN), for corporate financial distress forecasting in fashion and textiles supply chains. Firstly, related concepts of LR, SVM and BPNN are introduced. Then, the forecasting results by LR are introduce...

We present a new approach to training back-propagation artificial neural nets (BP-ANN) based on regularization and cross-validation and on initialization by a logistic regression (LR) model. The new approach is expected to produce a BP-ANN predictor at least as good as the LR-based one. We have applied the approach to ten data sets of biomedical interest and systematically compared BP-ANN and L...

Logistic regression is a technique to map the input feature to the posterior probability for a binary class. The optimal parameter of regression function is obtained by maximizing log likelihood of training data. In this report, we implement two optimization techniques 1) stochastic gradient decent (SGD); 2) limited-memory BroydenFletcherGoldfarbShanno (L-BFGS) to optimize the log likelihood fu...

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...

Logistic regression is the most common method used to model binary response data. When the response is binary, it typically takes the form of 1/0, with 1 generally indicating a success and 0 a failure. However, the actual values that 1 and 0 can take vary widely, depending on the purpose of the study. For example, for a study of the odds of failure in a school setting, 1 may have the value of f...

The  present  study  aimed  at  investigating  DIF  sources  on  an  EFL  reading  comprehension test.  Accordingly,  2  DIF  detection  methods,  logistic  regression  (LR)  and  item  response theory  (IRT),  were  used  to  flag  emergent  DIF  of  203  (110  females  &  93  males)  Iranian EFL examinees’ performance on a reading comprehension test. Seven hypothetical DIF sources were examin...