Ridge Regression and multicollinearity: An in-depth review

Schreiber-Gregory, Deanna

doi:10.3233/mas-180446

Cited by 48 publications

(31 citation statements)

References 2 publications

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“…No prior significance was assumed. Model assumptions were checked using variance inflation factors to measure collinearity among variables, 35 , 36 with variance inflation factors greater than 5 used as a cutoff for potential multicollinearity. 37 Likelihood ratio tests and the Akaike Information Criterion were used to validate the use of the full variable set by comparing against nested models.…”

Section: Methodsmentioning

confidence: 99%

Predictors of 30-Day Mortality Among Dutch Patients Undergoing Colorectal Cancer Surgery, 2011-2016

Bosch

Warps

Babberich³

et al. 2021

JAMA Netw Open

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IMPORTANCEQuality improvement programs for colorectal cancer surgery have been introduced with benchmarking based on quality indicators, such as mortality. Detailed (pre)operative characteristics may offer relevant information for proper case-mix correction. OBJECTIVE To investigate the added value of machine learning to predict quality indicators for colorectal cancer surgery and identify previously unrecognized predictors of 30-day mortality based on a large, nationwide colorectal cancer registry that collected extensive data on comorbidities. DESIGN, SETTING, AND PARTICIPANTS All patients who underwent resection for primary colorectal cancer registered in the Dutch ColoRectal Audit between January 1, 2011, and December 31, 2016, were included. Multiple machine learning models (multivariable logistic regression, elastic net regression, support vector machine, random forest, and gradient boosting) were made to predict quality indicators. Model performance was compared with conventionally used scores. Risk factors were identified by logistic regression analyses and Shapley additive explanations (ie, SHAP values). Statistical analysis was performed between March 1 and September 30, 2020. MAIN OUTCOMES AND MEASURES The primary outcome of this cohort study was 30-day mortality. Prediction models were trained on a training set by performing 5-fold cross-validation, and outcomes were measured by the area under the receiver operating characteristic curve on the test set. Machine learning was further used to identify risk factors, measured by odds ratios and SHAP values. RESULTSThis cohort study included 62 501 records, most patients were male (35 116 [56.2%]), were aged 61 to 80 years (41 560 [66.5%]), and had an American Society of Anesthesiology score of II (35 679 [57.1%]). A 30-day mortality rate of 2.7% (n = 1693) was found. The area under the curve of the best machine learning model for 30-day mortality (0.82; 95% CI, 0.79-0.85) was significantly higher than the American Society of Anesthesiology score (0.74; 95% CI, 0.71-0.77; P < .001), Charlson Comorbidity Index (0.66; 95% CI, 0.63-0.70; P < .001), and preoperative score to predict postoperative mortality (0.73; 95% CI, 0.70-0.77; P < .001). Hypertension, myocardial infarction, chronic obstructive pulmonary disease, and asthma were comorbidities with a high risk for increased mortality. Machine learning identified specific risk factors for a complicated course, intensive care unit admission, prolonged hospital stay, and readmission. Laparoscopic surgery was associated with a decreased risk for all adverse outcomes. CONCLUSIONS AND RELEVANCEThis study found that machine learning methods outperformed conventional scores to predict 30-day mortality after colorectal cancer surgery, identified specific (continued)

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Section: Methodsmentioning

confidence: 99%

Predictors of 30-Day Mortality Among Dutch Patients Undergoing Colorectal Cancer Surgery, 2011-2016

Bosch

Warps

Babberich³

et al. 2021

JAMA Netw Open

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“…To further combat the multicollinearity, we then tested the multiple regression model using the elastic nets method with Akaike's information selection criterion 43,44 , using as predictors of BIS the two remaining factors. This analysis yielded both factors of S_∆tHRV & ∆Midl-ϑPow and S_∆tHRV & ∆CzPz-αPow as potential predictors of BIS (F(2,59) = 9.01, p < 0.01, 2 p = 0.190; R-Square = 0.234; Glmselect procedure, SAS-9.4 45 ).…”

Section: Resultsmentioning

confidence: 99%

Placebo Analgesia Changes on Self-Pain and Empathy for Pain: Influences of Event-Related EEG Oscillations, Heart Rate Variability, and Personality

Pascalis

Vecchio

2021

Preprint

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We induced placebo analgesia (PA), a phenomenon explicitly attenuating the self-pain feeling, to assess whether this resulted in reduced empathy pain when witnessing a confederate undergoing such pain experience. We recorded EEG and electrocardiogram during a painful control and PA treatment in healthy adults who rated their experienced pain and empathy for pain. We derived HRV changes and, using wavelet analysis of non-phase-locked event-related EEG oscillations, EEG spectral power differences for self-pain and other-pain conditions. First-hand PA produced a reduction of self-pain and self-unpleasantness, whereas we observed only a slight decrease of other unpleasantness. We derived linear combinations of HRV and EEG band power changes significantly associated with self-pain and empathy for pain changes using PCAs. We found that relative HR-slowing together with decreased midline ϑ-band (4-8 Hz) power directly influenced self-pain reduction and, indirectly, through chained mediating effects of the Behavioral Inhibition System and Fight-Flight-Freezing System traits. In the other-pain condition, we detected a direct influence of the midline β2-band (22-30 Hz) power reduction on the other-pain decline with a positive mediating role of Total Empathic Ability. These findings suggest that PA modulation of first-hand versus other pain relies on functionally different physiological processes involving different personality traits.

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“…In addition, XGBoost implements a general tree-boosting algorithm. It adds Lasso (L1) [52] or Ridge (L2) [53] regulation to avoid an over-fitting, uses the second derivative information of the cost function, and introduces the idea of column sampling, as compared with GBDT. XGBoost significantly improves the efficiency and generalization of the prediction model.…”

Section: Machine-learning-based Model Fusionmentioning

confidence: 99%

Application of Machine-Learning-Based Fusion Model in Visibility Forecast: A Case Study of Shanghai, China

Wang

et al. 2021

Remote Sensing

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A visibility forecast model called a boosting-based fusion model (BFM) was established in this study. The model uses a fusion machine learning model based on multisource data, including air pollutants, meteorological observations, moderate resolution imaging spectroradiometer (MODIS) aerosol optical depth (AOD) data, and an operational regional atmospheric environmental modeling System for eastern China (RAEMS) outputs. Extreme gradient boosting (XGBoost), a light gradient boosting machine (LightGBM), and a numerical prediction method, i.e., RAEMS were fused to establish this prediction model. Three sets of prediction models, that is, BFM, LightGBM based on multisource data (LGBM), and RAEMS, were used to conduct visibility prediction tasks. The training set was from 1 January 2015 to 31 December 2018 and used several data pre-processing methods, including a synthetic minority over-sampling technique (SMOTE) data resampling, a loss function adjustment, and a 10-fold cross verification. Moreover, apart from the basic features (variables), more spatial and temporal gradient features were considered. The testing set was from 1 January to 31 December 2019 and was adopted to validate the feasibility of the BFM, LGBM, and RAEMS. Statistical indicators confirmed that the machine learning methods improved the RAEMS forecast significantly and consistently. The root mean square error and correlation coefficient of BFM for the next 24/48 h were 5.01/5.47 km and 0.80/0.77, respectively, which were much higher than those of RAEMS. The statistics and binary score analysis for different areas in Shanghai also proved the reliability and accuracy of using BFM, particularly in low-visibility forecasting. Overall, BFM is a suitable tool for predicting the visibility. It provides a more accurate visibility forecast for the next 24 and 48 h in Shanghai than LGBM and RAEMS. The results of this study provide support for real-time operational visibility forecasts.

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Ridge Regression and multicollinearity: An in-depth review

Cited by 48 publications

References 2 publications

Predictors of 30-Day Mortality Among Dutch Patients Undergoing Colorectal Cancer Surgery, 2011-2016

Predictors of 30-Day Mortality Among Dutch Patients Undergoing Colorectal Cancer Surgery, 2011-2016

Placebo Analgesia Changes on Self-Pain and Empathy for Pain: Influences of Event-Related EEG Oscillations, Heart Rate Variability, and Personality

Application of Machine-Learning-Based Fusion Model in Visibility Forecast: A Case Study of Shanghai, China

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