Outage duration prediction under typhoon disaster with stacking ensemble learning

ABSTRACTThe prediction of pollutants removal efficiency from the generated effluent of a treatment plant is valuable and can reduce the time, sampling and energy required during performance assessment. The present study aims to predict the effect of different input parameters on the treatment efficiency of the developed microbial‐based anaerobic process for textile effluent using machine leaning algorithms. The decolourisation and chemical oxygen demand (COD) reduction of the treated effluent were predicted on the basis of the three different input parameters pH, COD and colour value of the textile wastewater. The effectiveness of different machine learning algorithms, support vector machines (SVM), random forest (RF), gradient boost regressor (GBR), AdaBoost, extreme gradient boosting (XGB) regressor and voting regressor, were evaluated based on the correlation coefficient (R2) value. The results revealed that the RF achieved the highest accuracy for decolourisation (training data R2: ∼0.85 and test data R2: ∼0.84) as well as COD reduction (training data R2: ∼0.87 and test data R2: ∼0.94) compared to the other algorithms. These results were validated experimentally, confirming that RF can be used as a tool to predict the performance efficiency of a microbial‐based treatment system.

BackgroundDespite extensive research on keratoconus (KC) detection with traditional machine learning models, stacking ensemble learning approaches remain underexplored. This paper presents a stacking ensemble learning method to enhance automated KC screening.MethodsThis study utilizes a clinical dataset containing detailed corneal data from 2491 cases classified as non-KC (NKC), subclinical KC (SCKC) and clinical KC (CKC). Each cornea is represented by 79 features extracted from Pentacam imaging. Following extensive pre-processing, key corneal features that are strongly correlated with the target diagnosis are identified. These features are the keratometry of the steepest anterior point, surface variance index, vertical asymmetry index, height decentration index, and height asymmetry index. A novel stacking ensemble model is developed using the selected features to improve corneal classification into NKC, SCKC, and CKC by integrating top tree-based classifiers (random forest, gradient boosting, decision trees) with a support vector machine meta-classifier.ResultsThe pre-processing and feature selection techniques reduced the model's parameters to just 6.33% of the original dataset, improving classification performance, and cutting over 85% of the training time. The performance of the developed model was validated and tested on unseen data. Experimental results showed that the model outperforms existing studies, achieving 99.72% accuracy, precision, sensitivity, F1, and F2 scores, with a Matthews correlation coefficient of 0.995. It accurately classified all NKC and CKC cases, with just one misclassification involving an SCKC case. The model also demonstrated consistent performance on 100 additional unseen test cases, underscoring its generalizability and robustness in KC screening.ConclusionsBy combining the strengths of diverse base models and key Pentacam indices, the stacking ensemble approach ensures reliable, accurate KC screening, providing clinicians with an automated tool for early detection and better patient management.

Enhancing biomass Pyrolysis: Predictive insights from process simulation integrated with interpretable Machine learning models

Diagnosing a parasitic disease is a very difficult job in clinical practice. In this study, we constructed a machine learning model for diagnosis prediction using patient information. First, we diagnosed whether a patient has a parasitic disease. Next, we predicted the proper diagnosis method among the six types of diagnostic terms (biopsy, endoscopy, microscopy, molecular, radiology, and serology) if the patient has a parasitic disease. To make the datasets, we extracted patient information from PubMed abstracts from 1956 to 2019. We then used two datasets: the prediction for parasite-infected patient dataset (N = 8748) and the prediction for diagnosis method dataset (N = 3780). We then compared four machine learning models: support vector machine, random forest, multi-layered perceptron, and gradient boosting. To solve the data imbalance problem, the synthetic minority over-sampling technique and TomekLinks were used. In the parasite-infected patient dataset, the random forest, random forest with synthetic minority over-sampling technique, gradient boosting, gradient boosting with synthetic minority over-sampling technique, and gradient boosting with TomekLinks demonstrated the best performances (AUC: 79%). In predicting the diagnosis method dataset, gradient boosting with synthetic minority over-sampling technique was the best model (AUC: 87%). For the class prediction, gradient boosting demonstrated the best performances in biopsy (AUC: 88%). In endoscopy (AUC: 94%), molecular (AUC: 90%), and radiology (AUC: 88%), gradient boosting with synthetic minority over-sampling technique demonstrated the best performance. Random forest demonstrated the best performances in microscopy (AUC: 82%) and serology (AUC: 85%). We calculated feature importance using gradient boosting; age was the highest feature importance. In conclusion, this study demonstrated that gradient boosting with synthetic minority over-sampling technique can predict a parasitic disease and serve as a promising diagnosis tool for binary classification and multi-classification schemes.

<abstract> <p>This study presents a novel approach to enhance cardiovascular disease prediction using a hybrid machine learning (ML) model. Leveraging on Synthetic Minority oversampling techniques (SMOTE) and adaptive boosting (AdaBoost), we integrate these methods with prominent classifiers, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Extra Tree (ET). Focused on heart rate data as stress level indicators, our objective is to jointly predict cardiovascular disease, thereby addressing the global health challenge of early detection and accurate risk assessments. In response to class imbalance issues in cardiology databases, our hybrid model, which combines SMOTE and AdaBoost, demonstrates promising results. The inclusion of diverse classifiers, such as RF, XGBoost, and ET, enables the model to capture both linear and nonlinear relationships within the heart rate data, significantly enhancing the prediction accuracy. This powerful predictive tool empowers healthcare providers to identify individuals at a high risk for heart disease, thus facilitating timely interventions. This article underscores the pivotal role of ML and hybrid methodologies in advancing health research, particularly in cardiovascular disease prediction. By addressing the class imbalance and incorporating robust algorithms, our research contributes to the ongoing efforts to improve predictive modeling in healthcare. The findings presented here hold significance for medical practitioners and researchers striving for the early detection and prevention of cardiovascular diseases.</p> </abstract>

Hepatitis B virus (HBV) infection remains a public health concern in China, especially among middle-aged and older adults. Accurate prediction models can assist in early detection and targeted interventions. However, predicting HBV infection using population-level data is challenging due to its relatively low prevalence, which creates highly imbalanced datasets that undermine the performance of traditional predictive models. This study aimed to develop a machine learning model to predict HBV infection using an ensemble approach designed to handle class imbalance. Our study conducted a cross-sectional analysis of participants aged 45 and above from the China Health and Retirement Longitudinal Study 2011. To address the class imbalance in HBV infection, the synthetic minority oversampling technique (SMOTE) was employed. We developed machine learning models, including logistic regression (LR), support vector machine (SVM), random forest (RF), gradient boosting machine (GBM), adaptive boosting classifier (AdaBoost), extreme gradient boosting (XGBoost), and stacking ensemble model (SEM), to predict HBV infection and identify predictors. Model performance was evaluated using area under the receiver operating characteristic curve (AUC), area under the precision-recall curve (AP), accuracy, sensitivity, specificity, precision, F1 score, and Brier score. Shapley additive explanation (SHAP) analysis was applied to interpret each predictor’s individual contribution and direction to the model’s output. In the final analysis, 8,954 participants were included, among whom 0.64% self-reported HBV infection, confirming highly imbalanced data. The SEM model achieved balanced performance (AUC: 87.93%, AP: 18.85%, F1: 15.66%, Brier score: 0.03). Machine learning models also identified several new important predictors, including birth season, depression, and knee height. An ensemble machine learning model incorporating techniques to manage data imbalance can accurately predict HBV infection in Chinese middle-aged and older adults. The novel predictors we identified offer new insights into HBV risk in ageing populations and support the development of more targeted prevention strategies.

In the steam turbine reheating system, maintaining the high-pressure heater liquid level is crucial to prevent equipment damage and safety accidents. High liquid levels may cause steam to enter the feed system, while low liquid levels may lead to overheating and damage to the heater tube bundle. In this study, the stacking ensemble learning model for combined data decomposition is proposed for high-precision liquid level prediction and early warning. Initially, outliers in the high-pressure heater liquid level data from the DSC system are processed. The Adaptive Local Iterative Filtering (ALIF) algorithm is used for the first data decomposition, followed by Empirical Mode Decomposition (EMD) for a second decomposition. Time-domain features such as summation, maximum value, skewness, and peak value are extracted to construct reconstructed data. Random Forest (RF) reduces the dimensionality of the reconstructed data. The prediction model combines Deep Belief Network (DBN), Deep Neural Networks (DNN), Light Gradient Boosting Machine (LGBM), and eXtreme Gradient Boosting (xGBoost) as base models, with Echo State Network (ESN) as the meta-learner. Finally, based on historical data, warning level values are set, and the system outputs warnings when the actual liquid level significantly deviates from the predicted value.

Cheese-making traits in dairy cattle are important to the dairy industry but are difficult to measure at the individual level because there are limitations on collecting phenotypic information. Mid-infrared spectroscopy has its advantages, but it can only be used during monthly milk recordings. Recently, in-line devices for real-time analysis of milk quality have been developed. The AfiLab recording system (Afimilk) offers significant benefits as phenotypes can be collected from each cow at each milking session. The objective of this study was to assess the potential of integrating AfiLab real-time milk analyzer measures with the stacking ensemble learning technique using heterogeneous base learners for the in-line daily monitoring of cheese-making traits in Holstein cattle with a view to developing a precision livestock farming system for monitoring the technological quality of milk. Data and samples for wet-laboratory analyses were collected from 499 Holstein cows belonging to 2 farms where the AfiLab system was installed. The traits of concern were 9 milk coagulation traits [3 milk coagulation properties (MCP), and 6 curd firming traits (CFt)], and 7 cheese-making traits [3 cheese yield (CY) traits, and 4 milk nutrient recovery in the curd (REC) traits]. The near-infrared AfiLab spectral data and on-farm information (days in milk and parity) were used to assess the predictive ability of different statistical methods [elastic net (EN), gradient boosting machine (GBM), extreme gradient boosting (XGBoost), and artificial neural network (ANN)] across different cross-validation scenarios. These statistical methods were considered the base learners, which were then combined in a stacking ensemble learning. Results indicate that including information on the cows (days in milk and parity) in the AfiLab infrared prediction increased its accuracy by 10.3% for traditional MCP, 13.8% for curd firming, 9.8% for CY, and 11.2% for REC traits compared with those obtained from near-infrared AfiLab alone. The statistical approaches exhibited high prediction accuracies (R2) averaged across the cross-validation scenarios for traditional MCP (0.58 for ANN, 0.55 for EN and GBM, 0.52 for XGBoost, and 0.62 for stacking ensemble), CFt (0.55 for ANN, 0.54 for EN and GBM, 0.53 for XGBoost, and 0.61 for stacking ensemble), and similar R2 averages for CY and REC (0.55 for ANN, 0.54 for EN and GBM, 0.53 for XGBoost, and 0.61 for stacking ensemble). The ANN approach was more accurate than the other base learners (EN, GBM, and XGBoost) and improved accuracy across cross-validation scenarios on average by 7% for traditional MCP, 5% for CFt, 8% for CY, and 7% for REC. The stacking ensemble method improved prediction accuracy by 3% to 31% for traditional MCP, 2% to 26% for CFt, 1% to 38% for CY traits, and 2% to 27% for REC traits compared with the base learners. The prediction accuracies of the different approaches evaluated tended to decrease from the 10-fold cross-validation to the independent validation scenario, although there was a smaller reduction in prediction accuracy with the stacking ensemble learning technique across all the cross-validation scenarios. Our results show that combining in-line on-farm information with stacking ensemble machine learning represents an effective alternative for obtaining robust daily predictions of milk cheese-making traits.

Predicting nurse turnover is a growing challenge within the healthcare sector, profoundly impacting healthcare quality and the nursing profession. This study employs the Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance issues in the 2018 National Sample Survey of Registered Nurses dataset and predict nurse turnover using machine learning algorithms. Four machine learning algorithms, namely logistic regression, random forests, decision tree, and extreme gradient boosting, were applied to the SMOTE-enhanced dataset. The data were split into 80% training and 20% validation sets. Eighteen carefully selected variables from the database served as predictive features, and the machine learning model identified age, working hours, electric health record/electronic medical record, individual income, and job type as important features concerning nurse turnover. The study includes a performance comparison based on accuracy, precision, recall (sensitivity), F1-score, and AUC. In summary, the results demonstrate that SMOTE-enhanced random forests exhibit the most robust predictive power in the classical approach (with all 18 predictive variables) and an optimized approach (utilizing eight key predictive variables). Extreme gradient boosting, decision tree, and logistic regression follow in performance. Notably, age emerges as the most influential factor in nurse turnover, with working hours, electric health record/electronic medical record usability, individual income, and region also playing significant roles. This research offers valuable insights for healthcare researchers and stakeholders, aiding in selecting suitable machine learning algorithms for nurse turnover prediction.

Although the goal of rectal cancer treatment is to restore gastrointestinal continuity, some patients with rectal cancer develop a permanent stoma (PS) after sphincter-saving operations. Although many studies have identified the risk factors and causes of PS, few have precisely predicted the probability of PS formation before surgery. To validate whether an artificial intelligence model can accurately predict PS formation in patients with rectal cancer after sphincter-saving operations. Patients with rectal cancer who underwent a sphincter-saving operation at Taipei Medical University Hospital between January 1, 2012, and December 31, 2021, were retrospectively included in this study. A machine learning technique was used to predict whether a PS would form after a sphincter-saving operation. We included 19 routinely available preoperative variables in the artificial intelligence analysis. To evaluate the efficiency of the model, 6 performance metrics were utilized: accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiving operating characteristic curve. In our classification pipeline, the data were randomly divided into a training set (80% of the data) and a validation set (20% of the data). The artificial intelligence models were trained using the training dataset, and their performance was evaluated using the validation dataset. Synthetic minority oversampling was used to solve the data imbalance. A total of 428 patients were included, and the PS rate was 13.6% (58/428) in the training set. The logistic regression (LR), Gaussian Naïve Bayes (GNB), Extreme Gradient Boosting (XGB), Gradient Boosting (GB), random forest, decision tree and light gradient boosting machine (LightGBM) algorithms were employed. The accuracies of the logistic regression (LR), Gaussian Naïve Bayes (GNB), Extreme Gradient Boosting (XGB), Gradient Boosting (GB), random forest (RF), decision tree (DT) and light gradient boosting machine (LightGBM) models were 70%, 76%, 89%, 93%, 95%, 79% and 93%, respectively. The area under the receiving operating characteristic curve values were 0.79 for the LR model, 0.84 for the GNB, 0.95 for the XGB, 0.95 for the GB, 0.99 for the RF model, 0.79 for the DT model and 0.98 for the LightGBM model. The key predictors that were identified were the distance of the lesion from the anal verge, clinical N stage, age, sex, American Society of Anesthesiologists score, and preoperative albumin and carcinoembryonic antigen levels. Integration of artificial intelligence with available preoperative data can potentially predict stoma outcomes after sphincter-saving operations. Our model exhibited excellent predictive ability and can improve the process of obtaining informed consent.

Determination of malathion content in sorghum grains using hyperspectral imaging technology combined with stacked machine learning models

ObjectiveThis study aims to develop and compare different models to predict the Length of Stay (LoS) and the Prolonged Length of Stay (PLoS) of inpatients admitted through the emergency department (ED) in general patient settings. This aim is not only to promote any specific model but rather to suggest a decision-supporting tool (i.e., a prediction framework).MethodsWe analyzed a dataset of patients admitted through the ED to the “Sant”Orsola Malpighi University Hospital of Bologna, Italy, between January 1 and October 26, 2022. PLoS was defined as any hospitalization with LoS longer than 6 days. We deployed six classification algorithms for predicting PLoS: Random Forest (RF), Support Vector Machines (SVM), Gradient Boosting (GB), AdaBoost, K-Nearest Neighbors (KNN), and logistic regression (LoR). We evaluated the performance of these models with the Brier score, the area under the ROC curve (AUC), accuracy, sensitivity (recall), specificity, precision, and F1-score. We further developed eight regression models for LoS prediction: Linear Regression (LR), including the penalized linear models Least Absolute Shrinkage and Selection Operator (LASSO), Ridge and Elastic-net regression, Support vector regression, RF regression, KNN, and eXtreme Gradient Boosting (XGBoost) regression. The model performances were measured by their mean square error, mean absolute error, and mean relative error. The dataset was randomly split into a training set (70%) and a validation set (30%).ResultsA total of 12,858 eligible patients were included in our study, of whom 60.88% had a PloS. The GB classifier best predicted PloS (accuracy 75%, AUC 75.4%, Brier score 0.181), followed by LoR classifier (accuracy 75%, AUC 75.2%, Brier score 0.182). These models also showed to be adequately calibrated. Ridge and XGBoost regressions best predicted LoS, with the smallest total prediction error. The overall prediction error is between 6 and 7 days, meaning there is a 6–7 day mean difference between actual and predicted LoS.ConclusionOur results demonstrate the potential of machine learning-based methods to predict LoS and provide valuable insights into the risks behind prolonged hospitalizations. In addition to physicians' clinical expertise, the results of these models can be utilized as input to make informed decisions, such as predicting hospitalizations and enhancing the overall performance of a public healthcare system.

The mechanical characteristics of rocks, specifically uniaxial compressive strength (UCS) and elastic modulus (E), serve as crucial factors in ensuring the integrity and stability of relevant projects in mining and civil engineering. This study proposes a novel hybrid PSO (particle swarm optimization) with tree-based models, such as gradient boosting regressor (GBR), light gradient boosting machine (LightGBM), random forest (RF), and extreme gradient boosting (XGBoost) for predicting UCS and E of rock samples from Block IX of the Thar Coalfield in Pakistan. A total of 122 datasets were divided into training and testing sets, with an 80:20 ratio, respectively, to develop the predictive models. Key performance metrics, including the coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE), were employed to assess the model’s predictive performance. The results indicate that the PSO-XGBoost model demonstrated the highest accuracy in predicting UCS and E, outperforming the other models, which exhibited inferior predictive performance. Furthermore, this study utilized the SHAP (Shapley Additive exPlanations) machine learning method to enhance our understanding of how each input feature variable influences the output values of UCS and E. In conclusion, the proposed framework offers significant advantages in evaluating the strength and deformation of rocks at Thar Coalfield, with promising applications in the field of mining and rock engineering.

This study investigates the use of machine learning (ML) models combined with a Synthetic Minority Over-sampling Technique (SMOTE) and its variants to predict perioperative pressure injuries (PIs) in an imbalanced dataset. PIs are a significant healthcare problem, often leading to prolonged hospitalization and increased medical costs. Conventional risk assessment scales are limited in their ability to predict PIs accurately, prompting the exploration of ML techniques to address this challenge.We utilized data from 7,292 patients admitted to a tertiary care hospital in Shanghai between May 2017 and July 2023, with a final dataset of 2,972 patients, including 158 with PIs. Seven ML algorithms-Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Extra Trees (ET), K-Nearest Neighbors (KNN), and Decision Trees (DT)-were used in conjunction with SMOTE, SMOTE+ENN, Borderline-SMOTE, ADASYN, and GAN to balance the dataset and improve model performance.Results revealed significant improvements in model performance when SMOTE and its variants were used. For instance, the XGBoost model hadan AUC of 0.996 with SMOTE, compared to 0.800 on raw data. SMOTE+ENN and Borderline-SMOTE further enhanced the models' ability to identify minority classes. External validation indicatedthat XGBoost, RF, and ET exhibited the highest stability and accuracy, with XGBoost having an AUC of 0.977. SHAP analysis revealed that factors such as anesthesia grade, age, and serum albumin levels significantly influenced model predictions.In conclusion, integrating SMOTE with ML algorithms effectively addressed a data imbalance and improved the prediction of perioperative PIs. Future work should focus on refining SMOTE techniques and exploring their application to larger, multi-center datasets to enhance the generalizability of these findings, and especially for diseaseswith a lowincidence.

Photodetectors are essential components in modern optoelectronic technologies, yet experimental characterization of nanomaterial-based devices is often time-consuming and resource-intensive. To address this challenge, this study presents a stacking ensemble learning approach to predict the performance of bismuth-doped graphene quantum dots-based photodetectors under illumination levels ranging from 22 to 110 mW/mm². Four boosting algorithms—Adaptive Boosting, Gradient Boosting, Extreme Gradient Boosting, and Categorical Boosting—were trained on datasets obtained under dark, 22, 66, and 110 mW/mm², while 44 and 88 mW/mm² data were reserved for testing. A stacking ensemble learning model further enhanced prediction accuracy. The final model achieved a coefficient of determination of 0.9874 and a mean absolute error of 0.1840 at 88 mW/mm², effectively predicting the logarithmic current–voltage characteristics. The model also estimated key photodetector metrics, including sensitivity (1589.27), responsivity (2.389 mA/W), and specific detectivity (1.16 × 10¹⁰ Jones). This study explores the use of a stacking ensemble of four boosting algorithms to model the performance of Bi-GQD/p-Si photodetectors across different illumination levels, offering a data-driven alternative to traditional characterization.