An empirical evaluation of the Random Forests classifier models for variable selection in a large-scale lung cancer case-control study

Random Forests™ is reported to be one of the most accurate classification algorithms in complex data analysis. It shows excellent performance even when most predictors are noisy and the number of variables is much larger than the number of observations. In this thesis Random Forests was applied to a large-scale lung cancer case-control study. A novel way of automatically selecting prognostic factors was proposed. Also, synthetic positive control was used to validate Random Forests method. Throughout this study we showed that Random Forests can deal with large number of weak input variables without overfitting. It can account for non-additive interactions between these input variables. Random Forests can also be used for variable selection without being adversely affected by collinearities. ^ Random Forests can deal with the large-scale data sets without rigorous data preprocessing. It has robust variable importance ranking measure. Proposed is a novel variable selection method in context of Random Forests that uses the data noise level as the cut-off value to determine the subset of the important predictors. This new approach enhanced the ability of the Random Forests algorithm to automatically identify important predictors for complex data. The cut-off value can also be adjusted based on the results of the synthetic positive control experiments. ^ When the data set had high variables to observations ratio, Random Forests complemented the established logistic regression. This study suggested that Random Forests is recommended for such high dimensionality data. One can use Random Forests to select the important variables and then use logistic regression or Random Forests itself to estimate the effect size of the predictors and to classify new observations. ^ We also found that the mean decrease of accuracy is a more reliable variable ranking measurement than mean decrease of Gini. ^

Intensive care noise and mean arterial blood pressure in ELBW neonates

Objective. Loud noises in neonatal intensive care units (NICUs) may impede growth and development for extremely low birthweight (ELBW, < 1000 grams) newborns. The objective of this study was to measure the association between NICU sound levels and ELBW neonates' arterial blood pressure to determine whether these newborns experience noise-induced stress. ^ Methods. Noise and arterial blood pressure recordings were collected for 9 ELBW neonates during the first week of life. Sound levels were measured inside the incubator, and each subject's arterial blood pressures were simultaneously recorded for 15 minutes (at 1 sec intervals). Time series cross-correlation functions were calculated for NICU noise and mean arterial blood pressure (MABP) recordings for each subject. The grand mean noise-MABP cross-correlation was calculated for all subjects and for lower and higher birthweight groups for comparison. ^ Results. The grand mean noise-MABP cross-correlation for all subjects was mostly negative (through 300 sec lag time) and nearly reached significance at the 95% level at 111 sec lag (mean r = -0.062). Lower birthweight newborns (454-709 g) experienced significant decreases in blood pressure with increasing NICU noise after 145 sec lag (peak r = -0.074). Higher birthweight newborns had an immediate negative correlation with NICU sound levels (at 3 sec lag, r = -0.071), but arterial blood pressures increased to a positive correlation with noise levels at 197 sec lag (r = 0.075). ^ Conclusions. ELBW newborns' arterial blood pressure was influenced by NICU noise levels during the first week of life. Lower birthweight newborns may have experienced an orienting reflex to NICU sounds. Higher birthweight newborns experienced an immediate orienting reflex to increasing sound levels, but arterial blood pressure increased approximately 3 minutes after increases in noise levels. Increases in arterial blood pressure following increased NICU sound levels may result from a stress response to noise. ^