Comparative study of Decision Tree, Random Forest and XGBoost performance in forecasting the power output of a photovoltaic system

Didavi, Audace B. K.; Agbokpanzo, Richard Gilles; Agbomahéna, Macaire

doi:10.1109/biosmart54244.2021.9677566

Cited by 5 publications

(1 citation statement)

References 12 publications

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“…Moreover, deep learning models like neural networks usually have a large number of parameters to estimate and have poor generalizability without sufficient training data, and it takes longer to train them than XGBoost [ 37 ]. The random forest model is another widely applied machine learning approach, but XGBoost might be a better option for imbalanced data sets, such as the one used in our study [ 38 , 39 ].…”

Section: Discussionmentioning

confidence: 99%

Real-Time Prediction of Sepsis in Critical Trauma Patients: Machine Learning–Based Modeling Study

Li¹,

Xi²,

Yu³

et al. 2023

JMIR Form Res

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Background Sepsis is a leading cause of death in patients with trauma, and the risk of mortality increases significantly for each hour of delay in treatment. A hypermetabolic baseline and explosive inflammatory immune response mask clinical signs and symptoms of sepsis in trauma patients, making early diagnosis of sepsis more challenging. Machine learning–based predictive modeling has shown great promise in evaluating and predicting sepsis risk in the general intensive care unit (ICU) setting, but there has been no sepsis prediction model specifically developed for trauma patients so far. Objective To develop a machine learning model to predict the risk of sepsis at an hourly scale among ICU-admitted trauma patients. Methods We extracted data from adult trauma patients admitted to the ICU at Beth Israel Deaconess Medical Center between 2008 and 2019. A total of 42 raw variables were collected, including demographics, vital signs, arterial blood gas, and laboratory tests. We further derived a total of 485 features, including measurement pattern features, scoring features, and time-series variables, from the raw variables by feature engineering. The data set was randomly split into 70% for model development with stratified 5-fold cross-validation, 15% for calibration, and 15% for testing. An Extreme Gradient Boosting (XGBoost) model was developed to predict the hourly risk of sepsis at prediction windows of 4, 6, 8, 12, and 24 hours. We evaluated model performance for discrimination and calibration both at time-step and outcome levels. Clinical applicability of the model was evaluated with varying levels of precision, and the potential clinical net benefit was assessed with decision curve analysis (DCA). A Shapley additive explanation algorithm was applied to show the effect of features on the prediction model. In addition, we trained an L2-regularized logistic regression model to compare its performance with XGBoost. Results We included 4603 trauma patients in the study, 1196 (26%) of whom developed sepsis. The XGBoost model achieved an area under the receiver operating characteristics curve (AUROC) ranging from 0.83 to 0.88 at the 4-to-24-hour prediction window in the test set. With a ratio of 9 false alerts for every true alert, it predicted 73% (386/529) of sepsis-positive timesteps and 91% (163/179) of sepsis events in the subsequent 6 hours. The DCA showed our model had a positive net benefit in the threshold probability range of 0 to 0.6. In comparison, the logistic regression model achieved lower performance, with AUROC ranging from 0.76 to 0.84 at the 4-to-24-hour prediction window. Conclusions The machine learning–based model had good discrimination and calibration performance for sepsis prediction in critical trauma patients. Using the model in clinical practice might help to identify patients at risk of sepsis in a time window that enables personalized intervention and early treatment.

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Section: Discussionmentioning

confidence: 99%