Application of Gradient Boosting Machine Learning Algorithms to Predict Uniaxial Compressive Strength of Soft Sedimentary Rocks at Thar Coalfield

Endotracheal tube (ET) misplacement is common in pediatric patients, which can lead to the serious complication. It would be helpful if there is an easy-to-use tool to predict the optimal ET depth considering in each patient’s characteristics. Therefore, we plan to develop a novel machine learning (ML) model to predict the appropriate ET depth in pediatric patients. This study retrospectively collected data from 1436 pediatric patients aged < 7 years who underwent chest x-ray examination in an intubated state. Patient data including age, sex, height weight, the internal diameter (ID) of the ET, and ET depth were collected from electronic medical records and chest x-ray. Among these, 1436 data were divided into training (70%, n = 1007) and testing (30%, n = 429) datasets. The training dataset was used to build the appropriate ET depth estimation model, while the test dataset was used to compare the model performance with the formula-based methods such as age-based method, height-based method and tube-ID method. The rate of inappropriate ET location was significantly lower in our ML model (17.9%) compared to formula-based methods (35.7%, 62.2%, and 46.6%). The relative risk [95% confidence interval, CI] of an inappropriate ET location compared to ML model in the age-based, height-based, and tube ID-based method were 1.99 [1.56–2.52], 3.47 [2.80–4.30], and 2.60 [2.07–3.26], respectively. In addition, compared to ML model, the relative risk of shallow intubation tended to be higher in the age-based method, whereas the risk of the deep or endobronchial intubation tended to be higher in the height-based and the tube ID-based method. The use of our ML model was able to predict optimal ET depth for pediatric patients only with basic patient information and reduce the risk of inappropriate ET placement. It will be helpful to clinicians unfamiliar with pediatric tracheal intubation to determine the appropriate ET depth.

Section: Methodsmentioning

confidence: 99%

Predicting the risk of inappropriate depth of endotracheal intubation in pediatric patients using machine learning approaches

Shim

Lee

et al. 2023

“…Machine learning (ML) is a type of artificial intelligence (AI) that enables software applications to become more accurately predict outcomes without being explicitly programmed 26 , 27 . ML algorithms give us historical data as inputs to predict new output values.…”

Section: Methodsmentioning

confidence: 99%

“…In other words, using these methods is more about characterizing the data 32 . Supervised learning has higher accuracy than unsupervised learning, and the data of this study are labeled since it has a specific predictor value and a specific soil corrosion current density 27 . Therefore, the supervised learning algorithm is completely feasible for this study.…”

Section: Methodsmentioning

confidence: 99%

Machine learning modeling of predictive external corrosion rates of spent nuclear fuel carbon steel canister in soil

Chung

Yoo

et al. 2022

Soil corrosion is always a critical concern to corrosion engineering because of the economic influence of soil infrastructures as has been and has recently been the focus of spent nuclear fuel canisters. Besides corrosion protection, the corrosion prediction of the canister is also important. Advanced knowledge of the corrosion rate of spent nuclear fuel canister material in a particular environment can be extremely helpful in choosing the best protection method. Applying machine learning (ML) to corrosion rate prediction solves all the challenges because of the number of variables affecting soil corrosion. In this study, several algorithms of ML, including series individual, boosting, bagging artificial neural network (ANN), series individual, boosting, bagging Chi-squared automatic interaction detection (CHAID) tree decision, linear regression (LR) and an ensemble learning (EL) merge the best option that collects from 3 algorithm methods above. From the performance of each model to find the model with the highest accuracy is the ensemble stacking method. Mean absolute error performance matrices are shown in Fig. 15. Besides applying ML, the significance of the input variables was also determined through sensitivity analysis using the feature importance criterion, and the carbon steel corrosion rate is the most sensitive to temperature and chloride.

“…In the training and testing datasets, the BB estimation model based on t-SNE + Catboost has an RMSE = 0.260, MAE = 0.207, and RMSE = 0.282, MAE = 0.224, respectively. Shahani et al 72 developed four gradient boosting ML approaches including, extreme gradient boosting (XGBoost), Catboost, gradient boosted regression (GBR), and light gradient boosting machine (LightGBM) for estimating UCS of soft sedimentary rocks using a 106-point dataset. According to the findings, the XGBoost approach with statistical parameters such as RMSE: 0.00079 and MAE: 0.00062 in the training step and RMSE: 0.00069 and MAE: 0.00054 in the testing phase was affirmed to be the most precise approach among the four suggested approaches.…”

Section: Introductionmentioning

confidence: 99%

Modeling of H2S solubility in ionic liquids: comparison of white-box machine learning, deep learning and ensemble learning approaches

Mousavi

Nakhaei-Kohani

Atashrouz

et al. 2023

In the context of gas processing and carbon sequestration, an adequate understanding of the solubility of acid gases in ionic liquids (ILs) under various thermodynamic circumstances is crucial. A poisonous, combustible, and acidic gas that can cause environmental damage is hydrogen sulfide (H2S). ILs are good choices for appropriate solvents in gas separation procedures. In this work, a variety of machine learning techniques, such as white-box machine learning, deep learning, and ensemble learning, were established to determine the solubility of H2S in ILs. The white-box models are group method of data handling (GMDH) and genetic programming (GP), the deep learning approach is deep belief network (DBN) and extreme gradient boosting (XGBoost) was selected as an ensemble approach. The models were established utilizing an extensive database with 1516 data points on the H2S solubility in 37 ILs throughout an extensive pressure and temperature range. Seven input variables, including temperature (T), pressure (P), two critical variables such as temperature (Tc) and pressure (Pc), acentric factor (ω), boiling temperature (Tb), and molecular weight (Mw), were used in these models; the output was the solubility of H2S. The findings show that the XGBoost model, with statistical parameters such as an average absolute percent relative error (AAPRE) of 1.14%, root mean square error (RMSE) of 0.002, standard deviation (SD) of 0.01, and a determination coefficient (R2) of 0.99, provides more precise calculations for H2S solubility in ILs. The sensitivity assessment demonstrated that temperature and pressure had the highest negative and highest positive affect on the H2S solubility in ILs, respectively. The Taylor diagram, cumulative frequency plot, cross-plot, and error bar all demonstrated the high effectiveness, accuracy, and reality of the XGBoost approach for predicting the H2S solubility in various ILs. The leverage analysis shows that the majority of the data points are experimentally reliable and just a small number of data points are found beyond the application domain of the XGBoost paradigm. Beyond these statistical results, some chemical structure effects were evaluated. First, it was shown that the lengthening of the cation alkyl chain enhances the H2S solubility in ILs. As another chemical structure effect, it was shown that higher fluorine content in anion leads to higher solubility in ILs. These phenomena were confirmed by experimental data and the model results. Connecting solubility data to the chemical structure of ILs, the results of this study can further assist to find appropriate ILs for specialized processes (based on the process conditions) as solvents for H2S.