جستجوی مقالات مرتبط با کلیدواژه « Robust regression » در نشریات گروه « پزشکی »

 Machine learning is a class of modern and strong tools that can solve many important problems that nowadays humans may be faced with. Support vector regression (SVR) is a way to build a regression model which is an incredible member of the machine learning family. SVR has been proven to be an effective tool in real-value function estimation. As a supervised-learning approach, SVR trains using a symmetrical loss function, which equally penalizes high and low misestimates. Recently, high-dimensional datasets are the most challenging problem that may be faced. The main problems in high-dimensional data are the estimation of the coefficients and interpretation. In the high-dimension problems, classical methods are not applicable because of a large number of predictor variables. SVR is an excellent alternative method to analyze such datasets. One of the main advantages of SVR is that its computational complexity does not depend on the dimensionality of the input space. Additionally, it has excellent generalization capability, with high prediction accuracy.

 SVR is one of the best methods to analyze high-dimensional datasets. It is a really reliable and robust approach to have a good fit with high accuracy. SVR uses the same principles as the support vector machine for classification, with only a few minor differences.

 The techniques for analyzing the high-dimension datasets are really important methods because we frequently face such datasets in medical science and gene expression. It is not easy to analyze the high-dimension datasets because the classic methods cannot be used to estimate and interpret them. Therefore, we have to use alternative methods to analyze them. SVR is one of the best methods that can be applied. In this research, SVR is used in a real high-dimension dataset about the gene expression in eye disease, and then it is compared with well-knownmethods LASSO and Sparse least trimmed squared (sparse LTS) methods. Based on the numerical result, SVR and Sparse LTS were better than LASSO, since the real dataset contained outliers (bad observation with big residuals).

 SVR method was the best method to model and predict the high-dimensional mammalian eye dataset, because it was not affected by the outliers' corruptive impact, and it has minimum MSE (mean squares error), MAE (mean absolute error) and RMSE (root mean squared error) fitting criteria in comparison with the classical methods such as LASSO and sparse LTS estimations. Thus, sparse LTS was found to act better than the LASSO method. Moreover, stabilization of the data and freedom from obtaining the regularization parameter by running a complicated algorithmic program, which decreased the computational costs dramatically, were the invaluable advantages of this technique in comparison with the classical methods.

This study compared the outcomes of cognitive function assessments between pregnant and non-pregnant women groups to demonstrate alterations occurring during pregnancy. Furthermore, we aimed to determine the factors acting on cognitive functions in pregnant women. 

 42 pregnant and 42 non-pregnant women were included in the study. In order to compare cognitive performances, Montreal Cognitive Assessment test was applied to women. 

 The assessed scores of cognitive functioning were significantly different between pregnant and non-pregnant women (p <0.001). The test value was obtained as 22.29±4.57 with pregnants and as 26.02±2.19 with non-pregnant womens. The cognitive measurements yielded lower scores in the pregnant women. A negative correlation was found between the progesterone hormone levels and cognitive scores (p = 0.025). Progesterone hormone, TSH hormone and age of the pregnant were found to be important among the factors affecting the cognitive performances in pregnants (p=0.04; p=0.001; p=0.033, respectively). 

 Significant reductions in cognitive functions are observed in women during pregnancy. During pregnancy, in order to increase the cognitive level of women, hormonal values of pregnant women should be followed

By evolving science, knowledge, and technology, we deal with high-dimensional data in which the number of predictors may considerably exceed the sample size. The main problems with high-dimensional data are the estimation of the coefficients and interpretation. For high-dimension problems, classical methods are not reliable because of a large number of predictor variables. In addition, classical methods are affected by the presence of outliers and collinearity.

Nowadays, many real-world data sets carry structures of high-dimensional problems. To handle this problem, we used the least absolute shrinkage and selection operator (LASSO). Also, due to the flexibility and applicability of the semiparametric model in medical data, it can be used for modeling the genomic data. Motivated by these, here an improved robust approach in a high-dimensional data set was developed for the analysis of gene expression and prediction in the presence of outliers.

Among the common problems in regression analysis, there was the problem of outliers. In the regression concept, an outlier is a point that fails to follow the main linear pattern of the data. The ordinary least-squares estimator was found potentially sensitive to the outliers; this fact provided necessary motivations to investigate robust estimations. Generally, the robust regression is among the most popular problems in the statistics community. In the present study, the least trimmed squares (LTS) estimation was applied to overcome the outlier problem.

We have proposed an optimization approach for semiparametric models to combat outliers in the data set. Especially, based on a penalization LASSO scheme, we have suggested a nonlinear integer programming problem as the semiparametric model which can be effectively solved by any evolutionary algorithm. We have also studied a real-world application related to the riboflavin production. The results showed that the proposed method was reasonably efficient in contrast to the LTS Method.