A non-linear data mining parameter selection algorithm for continuous variables

In this article, we propose a new data mining algorithm, by which one can both capture the non-linearity in data and also find the best subset model. To produce an enhanced subset of the original variables, a preferred selection method should have the potential of adding a supplementary level of regression analysis that would capture complex relationships in the data via mathematical transformation of the predictors and exploration of synergistic effects of combined variables. The method that we present here has the potential to produce an optimal subset of variables, rendering the overall process of model selection more efficient. This algorithm introduces interpretable parameters by transforming the original inputs and also a faithful fit to the data. The core objective of this paper is to introduce a new estimation technique for the classical least square regression framework. This new automatic variable transformation and model selection method could offer an optimal and stable model that minimizes the mean square error and variability, while combining all possible subset selection methodology with the inclusion variable transformations and interactions. Moreover, this method controls multicollinearity, leading to an optimal set of explanatory variables

A non-linear data mining parameter selection algorithm for continuous variables

In this article, we propose a new data mining algorithm, by which one can both capture the non-linearity in data and also find the best subset model. To produce an enhanced subset of the original variables, a preferred selection method should have the potential of adding a supplementary level of regression analysis that would capture complex relationships in the data via mathematical transformation of the predictors and exploration of synergistic effects of combined variables. The method that we present here has the potential to produce an optimal subset of variables, rendering the overall process of model selection more efficient. This algorithm introduces interpretable parameters by transforming the original inputs and also a faithful fit to the data. The core objective of this paper is to introduce a new estimation technique for the classical least square regression framework. This new automatic variable transformation and model selection method could offer an optimal and stable model that minimizes the mean square error and variability, while combining all possible subset selection methodology with the inclusion variable transformations and interactions. Moreover, this method controls multicollinearity, leading to an optimal set of explanatory variables

Effect of chemotherapy on hippocampal volume and shape in older long-term breast cancer survivors

PurposeThe objective of this study was to assess changes in hippocampal volume and shape in older long-term breast cancer survivors who were exposed to chemotherapy 5–15 years prior.MethodsThis study recruited female long-term breast cancer survivors aged 65 years or older with a history of chemotherapy (C+), age-matched breast cancer survivors who did not receive chemotherapy (C−), and healthy controls (HC). The participants were recruited 5–15 years after chemotherapy at time point 1 (TP1) and were followed up for 2 years at time point 2 (TP2). Assessments included hippocampal volume and shape from brain MRI scans and neuropsychological (NP) tests.ResultsAt TP1, each of the three groups was comprised of 20 participants. The C+ group exhibited a hippocampal volume loss estimated in proportion with total intracranial volume (ICV) in both the left and right hemispheres from TP1 to TP2. Regarding the hippocampal shape at TP1, the C+ group displayed inward changes compared to the control groups. Within the C+ group, changes in right hippocampal volume adjusted with ICV were positively correlated with crystalized composite scores (R = 0.450, p = 0.044). Additionally, in C+ groups, chronological age was negatively correlated with right hippocampal volume adjusted with ICV (R = −0.585, p = 0.007).ConclusionThe observed hippocampal volume reduction and inward shape deformation within the C+ group may serve as neural basis for cognitive changes in older long-term breast cancer survivors with history of chemotherapy treatment

A non-linear data mining parameter selection algorithm for continuous variables

<div><p>In this article, we propose a new data mining algorithm, by which one can both capture the non-linearity in data and also find the best subset model. To produce an enhanced subset of the original variables, a preferred selection method should have the potential of adding a supplementary level of regression analysis that would capture complex relationships in the data via mathematical transformation of the predictors and exploration of synergistic effects of combined variables. The method that we present here has the potential to produce an optimal subset of variables, rendering the overall process of model selection more efficient. This algorithm introduces interpretable parameters by transforming the original inputs and also a faithful fit to the data. The core objective of this paper is to introduce a new estimation technique for the classical least square regression framework. This new automatic variable transformation and model selection method could offer an optimal and stable model that minimizes the mean square error and variability, while combining all possible subset selection methodology with the inclusion variable transformations and interactions. Moreover, this method controls multicollinearity, leading to an optimal set of explanatory variables.</p></div

Residual plot of our proposed method applied on .

<p>Note that the vertical axis is of the order 10⁻¹². The error perceived here is due to floating point and rounding error.</p

Algorithm 1 applied on <i>y</i> = 120 + 80<i>x</i>₁<i>x</i>₃.

<p>The horizontal axis shows the model found by our proposed method. The vertical axis shows the output <i>y</i>.</p