Lost circulation intensity characterization in drilling operations: Leveraging machine learning and well log data.

Child mortality is a big problem around the world, especially in low- and middle-income nations where there are big differences in health care and social conditions. This investigation seeks to create a predictive model for child mortality and pinpoint the key factors that significantly contribute to it, employing machine learning (ML) methodologies. The dataset includes various features such as parental age, maternal education, birth weight, wealth index, and access to healthcare services. Thirteen machine learning classifiers were used, categorized into four model groups: Traditional Models (Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Naive Bayes), Tree-Based Models (Decision Tree, Random Forest, Extra Trees), Boosting Models (AdaBoost, Gradient Boosting, XGBoost), and Ensemble Learning Models (Soft Voting, Hard Voting, Stacking). The efficacy of each model was assessed using classification metrics, including Accuracy, Precision, Recall, and F1-Score within a 10-fold cross-validation framework to guarantee robustness. Results indicate that ensemble models, particularly AdaBoost, achieved the highest predictive accuracy, with perfect scores across all metrics (1.00). XGBoost and Stacking also demonstrated strong and consistent performance. The findings indicate that ensemble learning methods are effective in predicting child mortality and can assist policymakers and healthcare planners in identifying high-risk populations and implementing targeted interventions to reduce child mortality.

Land degradation from gully erosion poses a significant threat to the Erer watershed in Eastern Ethiopia, particularly due to agricultural activities and resource exploitation. Identifying erosion-prone areas and underlying factors using advanced machine learning algorithms (MLAs) and geospatial analysis is crucial for addressing this problem and prioritizing adaptive and mitigating strategies. However, previous studies have not leveraged machine learning (ML) and GIS-based approaches to generate susceptibility maps identifying these areas and conditioning factors, hindering sustainable watershed management solutions. This study aimed to predict gully erosion susceptibility (GES) and identify underlying areas and factors in the Erer watershed. Four ML models, namely, XGBoost, random forest (RF), support vector machine (SVM), and artificial neural network (ANN), were integrated with geospatial analysis using 22 geoenvironmental predictors and 1,200 inventory points (70% used for training and 30% for testing). Model performance and robustness were validated through the area under the curve (AUC), accuracy, precision, sensitivity, specificity, kappa coefficient, F1 score, and logarithmic loss. The relative slope position is most influential, with 100% importance in SVM and RF and 95% importance in XGBoost, while annual rainfall (AR) dominated ANN (100% importance). Notably, XGBoost demonstrated robustness and superior prediction/mapping, achieving an AUC of 0.97, 91% accuracy, 92% precision, and 81% kappa while maintaining a low logloss (0.0394). However, SVM excelled in classifying gully resistant/susceptible areas (97% sensitivity, 98% specificity, and 91% F1 score). The ANN model predicted the most areas with very high gully susceptibility (13.74%), followed by the SVM (11.69%), XGBoost (10.65%), and RF (7.85%) models, while XGBoost identified the most areas with very low susceptibility (70.19%). The ensemble technique was employed to further enhance GES modeling, and it outperformed the individual models, achieving an AUC of 0.99, 93.5% accuracy, 92.5% precision, 97.5% sensitivity, 95.4% specificity, 85.8% kappa, and 94.9% F1 score. This technique also classified the GES of the watershed as 36.48% very low, 26.51% low, 16.24% moderate, 11.55% high, and 9.22% very high. Furthermore, district-level analyses revealed the most susceptible areas, including the Babile, Fedis, Harar, and Meyumuluke districts, with high GES areas of 32.4%, 21.3%, 14.3%, and 13.6%, respectively. This study offers robust and flexible ML models with comprehensive validation metrics to enhance GES modeling and identify gully prone areas and factors, thereby supporting decision-making for sustainable watershed conservation and land degradation prevention.

BackgroundDepression is a prevalent and challenging mental health condition, often resistant to traditional therapies. Repetitive transcranial magnetic stimulation (rTMS) is an FDA-approved treatment for resistant depression, but predicting treatment response remains difficult. Metabolic factors, such as obesity, diabetes, and associated inflammatory pathways, are increasingly recognized as influencing depression treatment outcomes. Leveraging machine learning may provide insights into how these factors predict rTMS efficacy, enabling more personalized treatments.Aims & ObjectivesThis study aims to identify key metabolic and clinical factors that predict rTMS treatment outcomes in patients with major depressive disorder (MDD) and bipolar disorder (BD). To achieve this, machine learning models were employed to develop predictive frameworks tailored for small participant groups.MethodSixty-eight patients (28 with BD) participated, undergoing 12 sessions of rTMS targeting the left dorsolateral prefrontal cortex. Participants were assessed at baseline, Week 4, and Week 12 using the Hamilton Depression Rating Scale (HDRS). Predictive models were developed using recursive feature elimination (RFE) and four machine learning algorithms: Random Forest, Gradient Boosting, Extra Trees, and XGBoost. Key features identified included body mass index (BMI), cholesterol ratios, and insulin resistance markers. SHAP (Shapley Additive Explanation) values were applied for model interpretation.ResultsAt Week 4, the remission rate was 59.6%, with BMI, systolic blood pressure (SBP), and LDL/HDL ratio emerging as significant predictors. The Extra Trees model achieved the highest performance (AUC = 0.766, accuracy = 0.726). At Week 12, remission was 61.5%, and predictors included insulin resistance (HOMA-IR), brain-derived neurotrophic factor (BDNF), and LDL/HDL ratio. The Gradient Boosting model excelled (AUC = 0.862, accuracy = 0.773). SHAP analysis highlighted BMI and HOMA-IR as the most influential factors at Weeks 4 and 12, respectively.Discussion & ConclusionsThe study underscores the impact of metabolic health on depression treatment outcomes. Metabolic dysregulation, particularly obesity and insulin resistance, may impair neuroplasticity and inflammation pathways, complicating treatment. BDNF's role in neurogenesis and metabolism was also highlighted as pivotal for recovery. Machine learning models demonstrated robust predictive performance, emphasizing the potential of integrating clinical and metabolic data to refine rTMS treatment approaches.Metabolic and clinical factors significantly influence rTMS outcomes in depression. Machine learning models, especially Extra Trees and Gradient Boosting, offer promising tools for individualized treatment planning. Future research should validate these findings in larger populations and explore additional biomarkers. Personalized approaches that account for metabolic health could reduce the trial-and-error nature of depression treatment and improve patient outcomes.

Mental stress is a growing global health concern, closely linked to psychological, behavioral, and physiological disorders. Accurate and early prediction of mental stress is crucial for timely interventions and improved health outcomes. Despite numerous studies leveraging machine learning (ML) techniques for stress classification, many have overlooked the integration of systematic feature selection and comprehensive model evaluation, limiting generalizability and interpretability. To address these gaps, this study proposes a robust ML-based framework that combines optimized feature selection methods - Recursive Feature Elimination (RFE), Extra Trees (ET), and Boruta - with various classification algorithms including Random Forest (RF), K-Nearest Neighbors (KNN), Decision Tree (DT), Multilayer Perceptron (MLP), Support Vector Machine (SVM), Gradient Boosting, and voting classifier. The models were evaluated using 10-fold cross-validation and ranked using the TOPSIS multi-criteria decision-making approach. The experimental results demonstrate high predictive performance across models (accuracy ≥ 0.98), with RF, DT, MLP, and Gradient Boosting achieving perfect accuracy (1.00). Among all configurations, the RF-Boruta model emerged as the most optimal (TOPSIS score: 0.914558). These findings highlight the effectiveness of combining systematic feature optimization with ML classification for accurate and interpretable stress prediction, offering valuable insights for data-driven mental health interventions.

The upcoming agricultural revolution, known as Agriculture 4.0, integrates cutting-edge Information and Communication Technologies in existing operations. Various cyber threats related to the aforementioned integration have attracted increasing interest from security researchers. Network traffic analysis and classification based on Machine Learning (ML) methodologies can play a vital role in tackling such threats. Towards this direction, this research work presents and evaluates different ML classifiers for network traffic classification, i.e., K-Nearest Neighbors (KNN), Support Vector Classification (SVC), Decision Tree (DT), Random Forest (RF) and Stochastic Gradient Descent (SGD), as well as a hard voting and a soft voting ensemble model of these classifiers. In the context of this research work, three variations of the NSL-KDD dataset were utilized, i.e., initial dataset, undersampled dataset and oversampled dataset. The performance of the individual ML algorithms was evaluated in all three dataset variations and was compared to the performance of the voting ensemble methods. In most cases, both the hard and the soft voting models were found to perform better in terms of accuracy compared to the individual models.

Background: Distinguishing ICU patients with candidaemia can help with the precise prescription of antifungal drugs to create personalized guidelines. Previous prediction models of candidaemia have primarily used traditional logistic models and had some limitations. In this study, we developed a machine learning algorithm trained to predict candidaemia in patients with new-onset systemic inflammatory response syndrome (SIRS).Methods: This retrospective, observational study used clinical information collected between January 2013 and December 2017 from three hospitals. The ICU patient data were used to train 4 machine learning algorithms–XGBoost, Support Vector Machine (SVM), Random Forest (RF), ExtraTrees (ET)–and a logistic regression (LR) model to predict patients with candidaemia.Results: Of the 8,002 cases of new-onset SIRS (in 7,932 patients) included in the analysis, 137 new-onset SIRS cases (in 137 patients) were blood culture positive for candidaemia. Risk factors, such as fungal colonization, diabetes, acute kidney injury, the total number of parenteral nutrition days and renal replacement therapy, were important predictors of candidaemia. The XGBoost machine learning model outperformed the other models in distinguishing patients with candidaemia [XGBoost vs. SVM vs. RF vs. ET vs. LR; area under the curve (AUC): 0.92 vs. 0.86 vs. 0.91 vs. 0.90 vs. 0.52, respectively]. The XGBoost model had a sensitivity of 84%, specificity of 89% and negative predictive value of 99.6% at the best cut-off value.Conclusions: Machine learning algorithms can potentially predict candidaemia in the ICU and have better efficiency than previous models. These prediction models can be used to guide antifungal treatment for ICU patients when SIRS occurs.

Hepatitis C is a viral infectious disease caused that affects 58 million people globally making it a public health crisis, and it is reported that 20-30% of cases develop to severe liver disease. This creates the need for data-driven based method that such as Machine learning that depends on data inputs in making accurate outputs with high precision. This study explores the use of Machine learning algorithms in optimizing the detection of hepatitis C status of patients. This research utilizes a publicly available dataset that contains 12 explanatory features and one target feature that addresses the patient’s diagnosis status. Data preprocessing techniques such as checking of missing values, assessment for possible outliers and Exploratory data analysis was carried on the dataset to improve the quality of the dataset for predictive analysis. The EDA results reviews that majority of the features had significant relationship (p-value < 0.05) with the target variable. Also, we observed the distribution of the disease diagnosis by gender reviews that females showed higher prevalence, and when we consider age, older people are more likely to contract the HCV. This result aligns with research findings from recent studies. The dataset was scaled and separated using a ratio split of 80:20 (80% of data for training). We applied SMOTE algorithm to the training data to balance the diagnosis distribution of the target variable. The model was trained using a wide parameter space using a cross-validation fold of 10 to select the best parameters. The ML models evaluated in this research include Logistic Regression, Support Vector Machine, Random Forest, XGBOOST, Decision Tree, and Extra Trees. The model performance metrics shows that XGBOOST was superior with an accuracy score of 98%, followed by Random Forest (97%), Decision Tree (94%), Extra Trees (93%), SVM (92%), and Logistic Regression (87%). The Kappa statistics for XGBOOST which was estimated as 0.8827 shows that difference between the predicted instances and the actual instances was minimal. The performance metrics of the XGBOOST was also contrast with ML algorithms trained by recent researchers in detecting HCV and it shows our model was more precise underscoring the significance of datapreprocessing, feature-scaling, SMOTE and hyperparameter tuning in predictive analysis in healthcare.

Undernutrition among children under the age of five is a major public health concern, especially in developing countries. This study aimed to use machine learning (ML) algorithms to predict undernutrition and identify its associated factors. Secondary data analysis of the 2017 Multiple Indicator Cluster Survey (MICS) was performed using R and Python. The main outcomes of interest were undernutrition (stunting: height-for-age (HAZ) < -2 SD; wasting: weight-for-height (WHZ) < -2 SD; and underweight: weight-for-age (WAZ) < -2 SD). Seven ML algorithms were trained and tested: linear discriminant analysis (LDA), logistic model, support vector machine (SVM), random forest (RF), least absolute shrinkage and selection operator (LASSO), ridge regression, and extreme gradient boosting (XGBoost). The ML models were evaluated using the accuracy, confusion matrix, and area under the curve (AUC) receiver operating characteristics (ROC). In total, 8564 children were included in the final analysis. The average age of the children was 926 days, and the majority were females. The weighted prevalence rates of stunting, wasting, and underweight were 17%, 7%, and 12%, respectively. The accuracies of all the ML models for wasting were (LDA: 84%; Logistic: 95%; SVM: 92%; RF: 94%; LASSO: 96%; Ridge: 84%, XGBoost: 98%), stunting (LDA: 86%; Logistic: 86%; SVM: 98%; RF: 88%; LASSO: 86%; Ridge: 86%, XGBoost: 98%), and for underweight were (LDA: 90%; Logistic: 92%; SVM: 98%; RF: 89%; LASSO: 92%; Ridge: 88%, XGBoost: 98%). The AUC values of the wasting models were (LDA: 99%; Logistic: 100%; SVM: 72%; RF: 94%; LASSO: 99%; Ridge: 59%, XGBoost: 100%), for stunting were (LDA: 89%; Logistic: 90%; SVM: 100%; RF: 92%; LASSO: 90%; Ridge: 89%, XGBoost: 100%), and for underweight were (LDA: 95%; Logistic: 96%; SVM: 100%; RF: 94%; LASSO: 96%; Ridge: 82%, XGBoost: 82%). Age, weight, length/height, sex, region of residence and ethnicity were important predictors of wasting, stunting and underweight. The XGBoost model was the best model for predicting wasting, stunting, and underweight. The findings showed that different ML algorithms could be useful for predicting undernutrition and identifying important predictors for targeted interventions among children under five years in Ghana.

Selecting the right crop plays very essential role in sustainable farming. Most of the time farmers make choices based on their experience instead of proper data, which is not always reliable. In this research, a machine learning– based system is introduced to find the best suitable crop on the basis of soil and environmental factors such as nitrogen (N), phosphorus (P), potassium (K), pH, temperature, humidity, and rainfall. For finding the most suitable crop, we used the six models: Random Forest, Gaussian Naive Bayes, Support Vector Classifier, Decision Tree, Logistic Regression, and Extra Trees. The result is calculated by different parameters such as accuracy, precision, recall, and F1-score. Out of these, Gaussian Naive Bayes gave the best result with 99.55% accuracy, followed by Random Forest with 99.32% and Decision Tree with 98.86% after that based on best model accuracy a simple web application was developed where users can input soil parameters and instantly they get the best crop suggestions. This shows how machine learning can support smart farming, improve crop yield, and reduce the old approach with so many error issues.

For proper design and operation of biomass‐based energy systems, it is important to determine the higher heating value (HHV) of biomass. In this paper, two machine learning (ML) approaches, namely extra trees (ET) and least squares support vector machine (LSSVM), are used to predict the value of HHV associated with biofuels. The data required for HHV calculation, including proximate and ultimate analyses datasets, were collected from the literature. The performances of these two ML approaches for predicting biomass HHV were then compared with other smart models available in the literature. Even though the available empirical models can predict the biomass HHV with acceptable precision, it was found that our proposed ML techniques have a superior performance based on the error analysis; the proposed approaches also consider all key biomass characteristics in the developed models. In addition, the ET model proved to be slightly more accurate compared to the LSSVM model. Additionally, the developed proximate‐based ET model showed better performance compared to the ultimate‐based ET model. The most influential parameters in the developed ET models for the proximate and ultimate approaches were determined to be ash fraction and carbon fraction, respectively. Finally, it was concluded that the smart modelling techniques can be utilized as a robust and reliable alternative predictive methodology to replace direct laboratory measurement of the biomass HHV.

Coal texture is an important factor in optimizing the characterization of coalbed methane (CBM) reservoirs, directly affecting key reservoir properties such as permeability, gas content, and production potential. This study develops an advanced methodology for coal texture classification in the Zhengzhuang Field of the Qinshui Basin, utilizing well-log data from 86 wells. Initially, 13 geophysical logging parameters were used to characterize the coal seams, resulting in a dataset comprising 2992 data points categorized into Undeformed Coal (UC), Cataclastic Coal (CC), and Granulated Coal (GC) types. After optimizing and refining the data, the dataset was reduced to 8 parameters, then further narrowed to 5 key features for model evaluation. Two primary scenarios were investigated: Scenario 1 included all 8 parameters, while Scenario 2 focused on the 5 most influential features. Five machine learning classifiers Extra Trees, Gradient Boosting, Support Vector Classifier (SVC), Random Forest, and k-Nearest Neighbors (kNN) were applied to classify coal textures. The Extra Trees classifier outperformed all other models, achieving the highest performance across both scenarios. Its peak performance was observed when 20% of the data was used for the test set and 80% for training, where it achieved a Macro F1 Score of 0.998. These findings demonstrate the potential of machine learning for improving coal texture prediction, offering valuable insights into reservoir characterization and enhancing the understanding of gas migration and accumulation processes. This methodology has significant implications for optimizing CBM resource evaluation and extraction strategies, especially in regions with limited sampling availability.

Domestic violence (DV) is a public health crisis that threatens both the mental and physical health of people. With the unprecedented surge in data available on the internet and electronic health record systems, leveraging machine learning (ML) to detect obscure changes and predict the likelihood of DV from digital text data is a promising area health science research. However, there is a paucity of research discussing and reviewing ML applications in DV research. Methods: We extracted 3588 articles from four databases. Twenty-two articles met the inclusion criteria. Results: Twelve articles used the supervised ML method, seven articles used the unsupervised ML method, and three articles applied both. Most studies were published in Australia (n = 6) and the United States (n = 4). Data sources included social media, professional notes, national databases, surveys, and newspapers. Random forest (n = 9), support vector machine (n = 8), and naïve Bayes (n = 7) were the top three algorithms, while the most used automatic algorithm for unsupervised ML in DV research was latent Dirichlet allocation (LDA) for topic modeling (n = 2). Eight types of outcomes were identified, while three purposes of ML and challenges were delineated and are discussed. Conclusions: Leveraging the ML method to tackle DV holds unprecedented potential, especially in classification, prediction, and exploration tasks, and particularly when using social media data. However, adoption challenges, data source issues, and lengthy data preparation times are the main bottlenecks in this context. To overcome those challenges, early ML algorithms have been developed and evaluated on DV clinical data.

Distributed Denial of Service (DDoS) attacks have emerged as one of the most pervasive and damaging threats to network security, disrupting services and incurring substantial financial costs. Machine learning (ML) has been widely explored as a potential solution to enhance DDoS detection and mitigation. This systematic review evaluates the effectiveness of ML techniques in detecting DDoS attacks, synthesizing findings from studies published between 2004 and 2024. The review analyzes models such as Random Forest, Support Vector Machines (SVM), and K-Nearest Neighbors (K-NN) based on key performance metrics like accuracy, precision, recall, and F1-score. A comprehensive search of multiple databases, including Web of Science, IEEE, Scopus, ScienceDirect, and Google Scholar, resulted in 19 studies that met inclusion criteria. The findings show that ensemble methods, particularly Random Forest, consistently outperformed other models in terms of detection rates, mainly due to their ability to handle large feature sets and reduce overfitting. Support Vector Machines also performed well in specific scenarios. However, their effectiveness was sometimes limited by computational complexity and the dataset size. K-Nearest Neighbors showed mixed results, depending on the nature of the attack patterns. This review emphasizes the potential of ensemble learning approaches for DDoS detection, demonstrating their robustness in dynamic environments. The review identifies key gaps in the existing research, including the need for better feature selection and exploring deep learning techniques to enhance DDoS detection accuracy and adaptability further. This study contributes valuable insights into the strengths and limitations of ML-based DDoS detection models, offering a foundation for future advancements in the field. It underscores the importance of continued research into hybrid and deep learning models to address the evolving and increasingly sophisticated nature of DDoS attacks in real-world applications.

Cesarean section (C-section) rates have been rising globally, posing potential health risks for mothers and infants. Understanding the factors that contribute to C-section delivery and leveraging machine learning (ML) techniques for predictive modeling can support targeted interventions and informed policy decisions. This study aimed to identify the determinants of C-section delivery and develop an ML-based predictive model using data from the Bangladesh Demographic and Health Survey (BDHS) 2022. A total of 2,490 complete records of ever-married women aged 15–49 years were analyzed, where the delivery mode was categorized as vaginal or C-section. Three feature selection techniques including Recursive Feature Elimination (RFE), Boruta-based selection (BFS), and Random Forest (RF) were used to identify key risk factors. Six ML algorithms, including Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), and Extreme Gradient Boosting (XGB) were employed to predict C-section. Model performance was evaluated using accuracy, precision, recall, F1-score, AUC, and ROC analysis. SHAP values were used to interpret the influence of individual features.The prevalence of C-section deliveries was 45.6%, with an average maternal age of 25.7 years and mean age at first childbirth of 19.3 years. Ten significant determinants were identified, including place of delivery, baby weight, maternal BMI, birth interval, age at first birth, partner’s education, maternal age, wealth status, ANC visits, and maternal education. The RF model achieved the highest performance with an accuracy of 81.79%, and an AUC of 0.871. SHAP analysis highlighted that place of delivery, baby weight, maternal BMI, and birth interval were the most influential predictors. These findings suggest that socio-demographic and healthcare-related factors strongly influence C-section delivery. Machine learning models particularly the RF can effectively identify women at high risk, supporting strategies to reduce unnecessary C-sections and improve maternal healthcare planning in Bangladesh.

Cancer survival classification using integrated data sets and intermediate information.