Machine learning-based prediction of drug lactation risk: Bridging molecular features and breastfeeding safety.

Due to the development of photovoltaic (PV) technology and the support from governments across the world, the conversion efficiency of solar energy has been improved. However, the PV power output is influenced by environment factors, resulting in features of randomness and intermittency. These features may have a negative influence on power systems. As a result, accurate and timely power prediction data is necessary for power grids to absorb solar energy. In this paper, we propose a new PV power prediction model based on the Gradient Boost Decision Tree (GBDT), which ensembles several binary trees by the gradient boosting ensemble method. The Gradient Boost method builds a strong learner by combining weak learners through iterative methods and the Decision Tree is a basic classification and regression method. As an ensemble machine learning algorithm, the Gradient Boost Decision Tree algorithm can offer higher forecast accuracy than one single learning algorithm. So GBDT is of value in both theoretical research and actual practice in the field of photovoltaic power prediction. The prediction model based on GBDT uses historical weather data and PV power output data to iteratively train the model, which is used to predict the future PV power output based on weather forecast data. Simulation results show that the proposed model based on GBDT has advantages of strong model interpretation, high accuracy, and stable error performance, and thus is of great significance for supporting the secure, stable and economic operation of power systems.

Laboratory-based hyperspectral imaging (HSI) is an optical non-destructive technology used to extract mineralogical information from bedrock drill cores. In the present study, drill core scanning in the long-wave infrared (LWIR; 8000–12,000 nm) wavelength region was used to map the dominant minerals in HSI pixels. Machine learning classification algorithms, including random forest (RF) and support vector machine, have previously been applied to the mineral characterization of drill core hyperspectral data. The objectives of this study are to expand semi-automated mineral mapping by investigating the mapping accuracy, generalization potential, and classification ability of cutting-edge methods, such as various ensemble machine learning algorithms and deep learning semantic segmentation. In the present study, the mapping of quartz, talc, chlorite, and mixtures thereof in HSI data was performed using the ENVINet5 algorithm, which is based on the U-net deep learning network and four decision tree ensemble algorithms, including RF, gradient-boosting decision tree (GBDT), light gradient-boosting machine (LightGBM), AdaBoost, and bagging. Prior to training the classification models, endmember selection was employed using the Sequential Maximum Angle Convex Cone endmember extraction method to prepare the samples used in the model training and evaluation of the classification results. The results show that the GBDT and LightGBM classifiers outperformed the other classification models with overall accuracies of 89.43% and 89.22%, respectively. The results of the other classifiers showed overall accuracies of 87.32%, 87.33%, 82.74%, and 78.32% for RF, bagging, ENVINet5, and AdaBoost, respectively. Therefore, the findings of this study confirm that the ensemble machine learning algorithms are efficient tools to analyze drill core HSI data and map dominant minerals. Moreover, the implementation of deep learning methods for mineral mapping from HSI drill core data should be further explored and adjusted.

Spam in emails has been a prevalent issue ever since the inception of the email service. However, the use of ensemble (aggregate) and non-ensemble algorithms for the detection and filtering of spam has been less explored. In this paper, we develop certain ensemble and non-ensemble machine learning (ML) algorithms for classifying emails as spam or ham (i.e., not spam). Using the Enron-SMS dataset from the UCI ML repository and an 80 and 20% training and test split, we develop and calibrate non-ensemble ML algorithms like KNN, Naive Bayes, and Support Vector Machine. Also, we develop and calibrate ensemble ML algorithms containing the non-ensemble algorithms via voting, bagging, and boosting methods. Results reveal that the non-ensemble Support Vector Machine performed the best with 98.47% accuracy on test data and it was followed by the ensemble voting algorithm with 96.80% accuracy on test data. We highlight the implications of using non-ensemble and ensemble methods for spam classification in the real world.

In this work, we train three decision-tree based ensemble machine learning algorithms (Random Forest Classifier, Adaptive Boosting and Gradient Boosting Decision Tree respectively) to study quasar selection in the variable source catalog in SDSS Stripe 82. We build training and test samples (both containing 1:1 of quasars and stars) using the spectroscopic confirmed sources in SDSS DR14 (including 8330 quasars and 3966 stars). We find that when trained with variation parameters alone, all three models can select quasars with similarly and remarkably high precision and completeness (∼ 98.5% and 97.5%), even better than trained with SDSS colors alone (∼ 97.2% and 96.5%), consistent with previous studies. By applying the trained models on the variable sources without spectroscopic identifications, we estimate the spectroscopically confirmed quasar sample in Stripe 82 variable source catalog is ∼ 93% complete (95% for mi < 19.0). Using the Random Forest Classifier we derive the relative importance of the observational features utilized for classifications. We further show that even using one- or two-year time domain observations, variability-based quasar selection could still be highly efficient.

A novel electromechanical impedance-based method for non-destructive evaluation of concrete fiber content

Atrial fibrillation (AF) is a life threatening disease and can cause stroke, heart failure, and sometimes death. To reduce the rate of mortality and morbidity due to increased prevalence of AF, early detection of the same becomes a prior concern. Traditional machine learning (TML) algorithms and ensemble machine learning (EML) algorithms are proposed to detect AF in this article. The performances of both these methods are compared in this study. Methodology involves computation of RR interval features extracted from electrocardiogram and its classification into: normal, AF, and other rhythms. TML techniques such as Classification and Regression Tree, K Nearest Neighbor, C4.5, Iterative Dichotomiser 3, Support Vector Machine and EML classifier such as Random Forest (RF), and Rotation Forest are used for classification. The proposed method is evaluated using PhysioNet challenge 2017. During the tenfold cross validation, it is observed that RF classifier provided good classification accuracy of 99.10% with area under the curve of 0.998. Apart from contributing a new methodology, the proposed study also experimentally proves higher performance with ensemble learning method, RF. The methodology has many applications in health care management systems including defibrillators, cardiac pacemakers, etc.

Background Children make up a large percentage of Coronavirus Disease 2019 (COVID-19) hospital admissions, but there is little information available about the features to predict the severity status of the illness or mortality in pediatrics. Logistic regression, supporting vector machine and ensemble machine learning algorithms were used to develop predictive models and identify prognostic factors for severity and mortality of COVID-19 in hospitalized children. Methods A total of 183 children with COVID-19 under the age of 18 years hospitalized in a referral hospital in Yazd province, Iran, from March 1, 2020 to August 1, 2021 were considered for this study. Logistic regression, and machine learning classifiers including supporting vector machine, decision tree, random forest, Bagging classifier trees, Gradient boosted decision trees, and Adaptive boost classifier trees were employed to predict the development of mild/severe or critical COVID-19 and death occurrence during hospitalization. Each model performance was assessed through five-fold cross-validation method, with evaluation metrics and area under the curve. In addition, the best clinical predictive models were used to identify significant factors between severe and non-severe groups, as well as between survivors and non-survivors. Results Seven predictive models were developed using the medical files of 183 hospitalized children, consisting of 94 and 89 (48.6%) in non-severe and severe groups, respectively, as well as 159 survivors and 24 (13%) non-survivors. In prediction of severity status, both decision tree and random forest algorithms had the highest accuracy of 73.3% and 68.7% to predict severity status in balanced data, respectively. Based on decision tree, respiratory distress and cough at the time of admission could be regarded as the as the key factors to estimate the likelihood of severity status. The results also showed that Gradient boosted decision trees, and Adaptive boost classifier trees had the best performance for mortality prediction in balanced data considering the accuracy of 88.8% and 87.7%, respectively. Cough at the time of admission, age group of 1–13 years old, and non-normal WBC could be considered as predictive factors for death occurrence. Conclusions This study indicated that tree-based classifiers were the best machine learning approaches for predicting severity status and mortality in hospitalized children with COVID-19. Clinical symptoms at the time of admission identified as the most predictive features though optimal algorithms.

Immunization is a cost-effective public health intervention globally, including in Ethiopia. However, the study focused on children aged 0–59 months and analyzed factors influencing incomplete immunization using ensemble machine learning techniques. A total of 16,394 EDHS datasets were used, with 80% for training and 20% for testing sets. Accordingly, the training set consisted of 13,115 samples, while the testing set contained 3,279 samples. Ensemble learning algorithms were employed, including Bagging methods (Bagging meta-estimator, Random Forest), Boosting methods (Gradient Boosting, XGBoost, LightGBM, AdaBoost, and CatBoost), and Voting ensembles combining both bagging and boosting models. Additionally, Stacking was performed using XGBoost and CatBoost as base models, with other machine learning algorithms such as Random Forest, K-Nearest Neighbors (KNN), Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Logistic Regression as meta-models. All models were implemented using the Python programming language. On the tested data, bagging meta-estimator + XGBoost voting model executed the highest performance result of accuracy (95.94%), f1-score (95.89%), recall (94.81%), and precision (97.07%), for visualizing using a confusion matrix and AUC-ROC value of 96%, and the cross-validation score of 95.75% for its reliability. Also, the most influential factors for incomplete immunization include marital status, residence, and others. This study aims to identify key factors influencing immunization coverage among Ethiopian children under the age of five and improve with ensemble machine learning algorithms. The findings provide valuable insights for targeted interventions, supporting improved immunization practices and contributing to better child health outcomes.

This study was designed to test if heart rate variability (HRV) data from preterm and full-term infants could be used to estimate their functional maturational age (FMA), using a machine learning model. We propose that the FMA, and its deviation from the postmenstrual age (PMA) of the infants could inform physicians about the progress of the maturation of the infants. The HRV data was acquired from 50 healthy infants, born between 25 and 41 weeks of gestational age, who did not present any signs of abnormal maturation relative to their age group during the period of observation. The HRV features were used as input for a machine learning model that uses filtering and genetic algorithms for feature selection, and an ensemble machine learning (EML) algorithm, which combines linear and random forest regressions, to produce as output a FMA. Using HRV data, the FMA had a mean absolute error of 0.93 weeks, 95% CI [0.78, 1.08], compared to the PMA. These results demonstrate that HRV features of newborn infants can be used by an EML model to estimate their FMA. This method was also generalized using respiration rate variability (RRV) and bradycardia data, obtaining similar results. The FMA, predicted either by HRV, RRV or bradycardia, and its deviation from the true PMA of the infants, could be used as a surrogate measure of the maturational age of the infants, which could potentially be monitored non-invasively and in real-time in the setting of neonatal intensive care units.

Purpose The present study aims to construct ensemble machine learning (EML) algorithms for groundwater potentiality mapping (GPM) in the Teesta River basin of Bangladesh, including random forest (RF) and random subspace (RSS). Design/methodology/approach The RF and RSS models have been implemented for integrating 14 selected groundwater condition parametres with groundwater inventories for generating GPMs. The GPM were then validated using the empirical and bionormal receiver operating characteristics (ROC) curve. Findings The very high (831–1200 km2) and high groundwater potential areas (521–680 km2) were predicted using EML algorithms. The RSS (AUC-0.892) model outperformed RF model based on ROC's area under curve (AUC). Originality/value Two new EML models have been constructed for GPM. These findings will aid in proposing sustainable water resource management plans.

Vegetation water content (VWC) is a crucial parameter for evaluating vegetation growth, climate change, natural disasters such as forest fires, and drought prediction. Spaceborne global navigation satellite system reflectometry (GNSS-R) has become a valuable tool for soil moisture (SM) and biomass remote sensing (RS) due to its higher spatial resolution compared with microwave measurements. Although previous studies have confirmed the enormous potential of spaceborne GNSS-R for vegetation monitoring, the utilization of this technology to fuse multiple RS parameters to retrieve VWC is not yet mature. For this purpose, this paper constructs a local high-spatiotemporal-resolution spaceborne GNSS-R VWC retrieval model that integrates key information, such as bistatic radar cross section (BRCS), effective scattering area, CYGNSS variables, and surface auxiliary parameters based on five ensemble machine learning (ML) algorithms (i.e., bagging tree (BT), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), random forest (RF), and light gradient boosting machine (LightGBM)). We extensively tested the performance of different models using SMAP ancillary data as validation data, and the results show that the root mean square errors (RMSEs) of the BT, XGBoost, RF, and LightGBM models in VWC retrieval are better than 0.50 kg/m2. Among them, the BT and RF models performed the best in localized VWC retrieval, with RMSE values of 0.50 kg/m2. Conversely, the XGBoost model exhibits the worst performance, with an RMSE of 0.85 kg/m2. In terms of RMSE, the RF model demonstrates improvements of 70.00%, 52.00%, and 32.00% over the XGBoost, LightGBM, and GBDT models, respectively.

IntroductionThe scale of default on credit bonds in China has been expanding. Credit bond defaults not only increase the financing costs of enterprises but also affect the efficiency of debt issuance and even lead to the spread of risks in the financial market. Accurately identifying bond default risks, clarifying the characteristics of bond defaults, and understanding the default risk mechanism are of crucial importance.MethodsThis paper takes corporate credit bonds as the research object and analyzes bond defaults from both macro and micro perspectives. From a macro perspective, it confirms the logical transmission between macro factors and bond defaults through causal relationships and grasps the overall characteristics of bond defaults by combining association rule mining and descriptive statistical research methods. Bonds are divided into a risk-free bond group and a risky bond group, and association rules are mined in four dimensions: the bond issuance region of the enterprise, whether the issuer is listed, the attributes of the issuing enterprise, and whether the enterprise bond is guaranteed. Based on these rules, a cross-analysis of bond risk factors is conducted. From a micro perspective, taking each bond as the research object, a bond default identification system is established, and default predictions are made based on the ensemble learning algorithm. The important characteristics of default bonds are analyzed from the perspective of whether the issuer is a state-owned enterprise, and further cause difference analysis is conducted.ResultsThe results show that M1 and M2 have an impact on bond defaults, and the ensemble machine learning algorithm can accurately predict bond default risks and obtain key factors for bond risk identification. It is reasonable to choose macro indicators to predict bond defaults.DiscussionBased on the experimental conclusions, this paper discusses and analyzes the bond risk evolution process and the reasons for risk concentration in certain industries, which is helpful for a comprehensive understanding of bond default risks. Our research can provide tool references and guidance for risk management in the actual bond market.

This study proposes a novel, new ensemble model (NEM) designed to simulate the maximum water level increases caused by storm surges in a frequently cyclone‐affected coastal water of Hong Kong, China. The model relies on storm and water level data spanning 1978–2022. The NEM amalgamates three machine learning algorithms: Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and XGBoost (XGB), employing a stacking technique for integration. Six parameters, determined using the Random Forest and Recursive Feature Elimination algorithms (RF‐RFE), are used as input features for the NEM. These parameters are the nearest wind speed, gale distance, nearest air pressure, minimum distance, maximum pressure drop within 24 hr, and large wind radius. Model assessment results suggest that the NEM exhibits superior performance over RF, GBDT, and XGB, delivering high stability and precision. It reaches a coefficient of determination (R2) up to 0.95 and a mean absolute error (MAE) that fluctuates between 0.08 and 0.20 m for the test data set. An interpretability analysis conducted using the SHapley Additive exPlanations (SHAP) method shows that gale distance and nearest wind speed are the most significant features for predicting peak water level increases during storm surges. The results of this study could provide practical implications for predictive models concerning storm surges. These findings present essential tools for the mitigation of coastal disasters and the improvement of marine disaster warning systems.

Volcanic lithology identification based on parameter-optimized GBDT algorithm: A case study in the Jilin Oilfield, Songliao Basin, NE China

Background:To develop and validate an ensemble machine learning ultrasound radiomics model for predicting drug resistance in lymph node tuberculosis (LNTB).Materials and methods:This multicenter study retrospectively included 234 cervical LNTB patients from one center, randomly divided into training (70%) and internal validation (30%) cohorts. Radiomic features were extracted from ultrasound images, and an L1-based method was used for feature selection. A predictive model combining ensemble machine learning and AdaBoost algorithms was developed to predict drug resistance. Model performance was assessed using independent external test sets (Test A and Test B) from two other centers, with metrics including AUC, accuracy, precision, recall, F1 score, and decision curve analysis.Results:Of the 851 radiometric features extracted, 161 were selected for the model. The model achieved AUCs of 0.998 (95% CI: 0.996–0.999), 0.798 (95% CI: 0.692–0.904), 0.846 (95% CI: 0.700–0.992), and 0.831 (95% CI: 0.688–0.974) in training, internal validation, and external test sets A and B, respectively. The decision curve analysis showed a substantial net benefit across a threshold probability range of 0.38 to 0.57.Conclusion:The LNTB resistance prediction model developed demonstrated high diagnostic efficacy in both internal and external validation. Radiomics, through the application of ensemble machine learning algorithms, provides new insights into drug resistance mechanisms and offers potential strategies for more effective patient treatment.Keywords:drug resistance, lymph node tuberculosis, machine learning, radiomics, ultrasound