Academic literature on the topic 'Backward stepwise'

Dehydration risk increases with frailty and functional dependency, but a limited number of studies have evaluated this association in hospitalized geriatric patients. This cross-sectional study aimed to assess the prevalence and determinants of dehydration in patients admitted to the geriatric ward. Dehydration was diagnosed when calculated osmolarity was above 295 mMol/L. Logistic regression analyses (direct and stepwise backward) were used to assess determinants of impending dehydration. 358 of 416 hospitalized patients (86.1%) were included: 274 (76.5%) women, and 309 (86.4%) 75+ year-old. Dehydration was diagnosed in 209 (58.4%) cases. Significantly higher odds for impending dehydration were observed only for chronic kidney disease with trends for diabetes and procognitive medication when controlling for several health, biochemical, and nutritional parameters and medications. After adjusting for “dementia” the negative effect of “taking procognitive medications” became a significant one. Chronic kidney disease, diabetes, taking procognitive medications and hypertension were the main variables for the outcome prediction according to the stepwise backward regression analysis. This may indicate an additional benefit of reducing the risk of dehydration when using procognitive drugs in older patients with dementia.

In this paper, the performances of four improved analytical methods (backward stepwise selection of peak intensities, sum of characteristic peaks of a component, moving window partial least squares, and genetic algorithms) using wavelength selection for the analysis of xylene mixtures by Raman spectroscopy are tested for further use on the new “digital micromirror device associated with a photomultiplier tube” Raman spectrometer. It is shown that the errors of prediction using only a few selected points (from 4 to 49 depending on the method) are almost the same as when using the whole spectral range (1050 points). Compared to the last two methods, the “backward stepwise selection of peak intensities” and “sum of characteristic peaks of a component” methods are robust under industrial conditions and appear to be well suited for chemical quantitative analysis with the new Raman spectrometer, which allows the measurements of the total intensity to be made simultaneously for a number of pre-selected frequencies. Results show that the errors of prediction can be near to or even lower than 2%.

This paper proposes different classification algorithms—logistic regression, support vector machine, K-nearest neighbors, and random forest—in order to identify which candidates are likely to default for a credit scoring model. Three different feature selection methods are used in order to mitigate the overfitting in the curse of dimensionality of these classification algorithms: one filter method (Chi-squared test and correlation coefficients) and two wrapper methods (forward stepwise selection and backward stepwise selection). The performances of these three methods are discussed using two measures, the mean absolute error and the number of selected features. The methodology is applied for a valuable database of Taiwan. The results suggest that forward stepwise selection yields superior performance in each one of the classification algorithms used. The conclusions obtained are related to those in the literature, and their managerial implications are analyzed.