A Data-Driven Comparative Study of Machine Learning Algorithms for Hepatitis C Diagnosis

Lost circulation is one of the important challenges in drilling operations and bears financial losses and operational risks. The prime causes of lost circulation are related to several geological parameters, especially in problem-prone formations. Herein, the approach of applying machine learning models to forecast the intensity of lost circulation using well-log data is presented in this work. It concerns a gas field in northern Iran and contains nine well logs with lost circulation incidents categorized into six intensity classes. After rigorous exploratory analysis and preprocessing of the data, seven machine learning methods are applied: Random Forest, Extra Trees, Decision Tree, XGBoost, k-Nearest Neighbors, Support Vector Machine, and Hard Voting. Random Forest, Extra Trees, and Hard Voting are the best-performing methods. These models attained the most robust results on both key performance metrics and, hence, can predict the intensity of lost circulation quite well. Models of Extra Trees and Hard Voting show very high predictive performance values. On the other hand, their limitations in some intensity classes suggest further refinement. In this regard, the ensemble methods are highly effective for managing the multivariate nature of the task. Hard Voting aggregates multiple classifiers, becoming superior to individual models like support vector machines. This paper offers new insight into integrating machine learning to well-log data toward enhancing lost circulation prediction by offering a dependable foundation for real-time drilling decision-making. These results show that the models have the potential to lower operational risks, improve drilling safety, and minimize nonproductive time; hence, they form a quantum leap in lost circulation control.

One of the most serious and alarming problems facing humanity is the degradation of natural water resources such as lakes as well as rivers is one of the most serious and vexing problems we are facing today. The long-term effects of polluted water affect all areas of life. Therefore, it is essential to manage water resources if you want tomaximize the quality of your water. If data are examinedand water quality can be predicted, the effects of watercontamination can be dealt with effectively. The purpose of this study is to use machine learning to make a water qualityprediction model based on water quality measurements. Machine learning can be used for building models fromalgorithms with some data gathered from the sick ones. For a better examination of parametric findings, the acquired data will be pre-processed, separated into training and testing parts, and subjected to machine learning classification techniques. Decision tree, Naive Bayes, Random Forest, Support Vector Machine, and K-Nearest Neighbor are some of the classification-type algorithms employed in this work. All the model's performance indicators are calculated, and they change for each model. A technique for improving machine learning model performance metrics is hyperparameter tuning.

In recent years, there has been a noticeable trend toward targeted threats to information security, where companies are now leveraging vulnerabilities and risks associated with widely used services in order to generate financial gain. Additionally, they implement numerous precautions and consistently carry out their tasks. One item that requires precautionary measures is the network devices utilized. Network devices in computer networks possess the capability to log events. These logs enable the identification of security events on the network and facilitate the implementation of precautionary measures. Various security measures can be implemented to handle such data. One of these measures is Security Information and Event Management (SIEM). It is a system that gathers and analyzes data from networks and security devices. SIEM is a technique employed to consolidate critical information within a cohesive structure. It allows for the correlation of events from different security devices, thereby improving the monitoring capabilities of cybersecurity operations centers. This study extensively covers the critical infrastructure-SIEM relationship, current studies, critical infrastructure, cyber security policies, and SIEM. Our system design was developed using the UNSW\_NB15 dataset, a widely recognized dataset in cybersecurity due to its comprehensive and realistic representation of cyber threats. This dataset consists of data obtained from network traffic, various attack activities, and real-life modern normal scenarios, making it particularly relevant to our study. With the studies, a total of 10 different categories were analyzed, with the category consisting of nine types of attacks, namely Analysis, Backdoor, DoS, Exploits, Fuzzers, Generic, Reconnaissance, Shellcode, and Worms and Normal activities. The study is divided into two as the basic structure. The first step was carried out on Google Collaboratory, and then some experimental studies were carried out in Weka. Classifications were made using several methods, including Logistic Regression (LR), Extra Trees (XT), Support Vector Machines (SVM), Random Forest (RF), and Decision Trees (DT). These methods were chosen for their proven effectiveness in similar studies. In the application developed with Google Colabratory, we achieved 98.62\% in Random Forest, 99.10\% in Decision Trees, 98.87\% in Logistic Regression, 95.13\% success in Extra Trees and 99.12\% success in Support Vector Machines. As a result of the studies and experiments carried out in Weka, we achieved 92.05\% in Random Forest, 100\% in Decision Trees, 100\% in k-Nearest Neighbours, 100\% in J48, 99.19\% in Naive-Bayes and 99.35\% in BayesNet achievements.

Prenatal prediction of CD34+ adequacy supports cord-blood banking by reducing expenses on low-yield units and reserving capacity for clinically promising grafts. We have developed and evaluated a prenatal machine learning model that predicts whether an umbilical cord blood (UCB) unit will meet a clinically supported adequacy threshold (≥1.5×105 CD34+ cells/kg recipient) before collection for single-unit grafts. In this retrospective study, we analysed 126,406 records from the Royan Stem Cell Technology Company (RSCT; Tehran, Iran), which included routinely available maternal, neonatal, and family-history variables. A pipeline of imputation (IterativeImputer numeric; SimpleImputer+OrdinalEncoder categorical), feature selection (Extra Trees), and hyperparameter tuning using Bayesian optimisation with model training/evaluation was performed within cross-validation folds. Decision Tree (DT), K-Nearest Neighbours (KNN), Random Forest (RF), Support Vector Machine (SVM), and Multilayer Perceptron (MLP) classifiers were tuned via Bayesian optimisation. Models were ranked by the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Majority voting (MV) ensembles were constructed from the top-k models. Model interpretability used SHapley Additive exPlanations (SHAP). The MV (top-4: RF, KNN, DT, MLP) ensemble achieved an area under the receiver operating characteristic curve (ROC-AUC)=0.808 and an area under the precision-recall curve (PR-AUC)=0.744 on the held-out test set, with an accuracy=0.757, precision=0.726, recall/sensitivity=0.804, F1=0.762, specificity=0.716, and Brier score=0.181. SHAP highlighted history of hepatitis C, birth place, hyperthyroidism, history of anaemia, oral fungus, and rheumatism among the most influential features. Prenatal prediction of UCB CD34+ adequacy using an interpretable MV ensemble is feasible and accurate to support pre-collection triage and can potentially improve banking efficiency. The resultant model offers a non-invasive tool to enhance the efficiency of cord blood banking by prioritising units with higher transplantation potential.

Diabetes is a non-communicable disease affecting people of all ages worldwide, therefore, early detection using machine learning techniques is crucial. This study aims to predict diabetes using multiple machine learning algorithms, performance metrics, and holdout validation on an Egyptian dataset. The dataset was divided into four age groups, including paediatric, early adulthood, middle age, and geriatric. Ten algorithms were applied and validated using 80:20, 70:30, and 60:40 split ratios with accuracy, precision, and recall as evaluation metrics. Results showed that Random Forest, Extra Trees, and Support Vector Machine performed best in the paediatric group, while Gradient Boosting, Random Forest, and Support Vector Machine achieved superior performance in early adulthood, middle age, and geriatric groups. In contrast, Decision Tree, K-Nearest Neighbors, and AdaBoost consistently demonstrated lower performance. Further analysis reveals that classification performance varies significantly across age groups, with the middle age and geriatric groups achieving the highest accuracy above 0.99, followed by the paediatric group 0.98–0.99, while early adulthood exhibits comparatively lower performance due to increased class overlap. Confusion matrix results indicate strong diagonal dominance in higher-performing groups, reflecting better class separability, whereas performance heatmaps confirm that top models maintain a balanced trade-off between accuracy, precision, and recall with minimal variation across different data splits. Feature importance analysis shows that higher performing models rely on a small number of dominant predictors, particularly in the middle age and geriatric groups, while more distributed feature contributions in early adulthood reduce predictive effectiveness. Therefore, the findings demonstrate that ensemble methods provide robust and consistent performance, and that age-based dataset segmentation enhances classification accuracy and model stability.

ObjectiveTo investigate a panel of variables using four machine learning based classifiers, i.e., support vector machine (SVM), random forest (RF), artificial neural network (ANN) and logistic regression (LR) to make a diagnosis of ovarian cancer, differentiating it from benign ovarian masses.Materials and methodsA prospective observational pilot study was done between November 2021 and June 2023. Following data pre-processing to ensure compatibility with ML models, four ML algorithms, i.e., support vector machine (SVM), logistic regression (LR), random forest (RF) and artificial neural network (ANN) were tested by multimodal parameters from the datasets of 50 patients presenting with suspected epithelial ovarian cancer (Group A) or benign ovarian tumour (Group B). Statistical analysis was done using STATA version 14.0.ResultsWe found that the machine learning approach could predict malignant tumours with appreciably high accuracy similar to a few studies done so far in this field. All four ML algorithms showed high level of accuracy with a maximum AUROC of 0.92 in the RF model. Both RF and SVM had an accuracy of 85.87 and 83.05%.ConclusionThe ML algorithms can detect ovarian cancers with a high level of accuracy. Further, a large-volume prospective study on large volume data sets is required before inclusion of ML algorithms in clinical practice.

Machine learning (ML) has recently been used as an efficient surrogate to estimate different steps of performance‐based earthquake engineering (PBEE), from dynamic structural analysis to fragility and loss assessments. However, due to the varied data, models, and features in existing literature, the relative efficiency of ML models across different PBEE steps remains unclear. Additionally, the black‐box nature of advanced ML algorithms limits their ability to provide design‐oriented insights, hindering the broader application of ML in PBEE‐based design. This study provides a comprehensive comparison of the accuracy and explainability of design‐oriented ML models across different steps of PBEE using a consistent database of 621 steel moment frames with varying designs and geometry. Eight ML algorithms were used in a careful training workflow comprising feature selection, hyperparameter tuning, cross‐validation, and model inference. The sensitivity of model accuracy to representative PBEE outputs—maximum responses, median fragility, and expected annual loss—was assessed using statistical measures. In addition, the explainability of the best models for each step was examined to explore the relationship between design parameters and the corresponding PBEE output. The results show that while ML models can reasonably map design parameters to all different PBEE outputs, models accuracy was higher for drift responses, median fragilities, and component‐based loss metrics. In addition, the optimal algorithm remained the same across different PBEE steps, where support vector machines and random forests provided the highest accuracy with an average R2 of 0.93 and 0.91 over different outputs on the test set. Although the selected feature sets varied across outputs and algorithms, height, number of stories, fundamental period, and the minimum of the beams’ moment of inertia were influential for both models and notably affected different PBEE outputs.

BackgroundTranscutaneous transhepatic gallbladder drainage (PTGBD) has shown significant efficacy in the treatment of elderly patients with acute cholecystitis. The goal of this study is to develop a machine learning-based web calculator aimed at predicting the optimal timing for cholecystectomy (LC) after PTGBD in elderly patients with acute cholecystitis (AC) to achieve precise personalized medicine.MethodsA retrospective analysis of 979 elderly patients with acute cholecystitis admitted to Jinzhou Central Hospital and the First Affiliated Hospital of Jinzhou Medical University from 2013 to 2024 was performed, and a total of 680 patients were included in the model development. Patients were divided into delayed (347 cases, surgery > 6 weeks post-PTGBD) and non-delayed (333 cases) groups based on the interval between PTGBD and LC. Minimal Absolute Contraction and Selection Operator (LASSO) and logistic analysis were used to determine the predictors of postponement of LC in elderly patients with AC after PTGBD. Next, we used eight ML algorithms, namely Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Extreme gradient boosting (XGB), Support Vector Machine (SVM), Multilayer Perceptron (MLP), K-nearest Neighbor (KNN), Gaussian Naive Bayes (GNB), to train and develop ML models using a 10x cross-validation method. The performance of the model was evaluated by a variety of indicators, including the area under the receiver operating characteristic curve (ROC), calibration curve, decision curve, PR curve, and confusion matrix. In addition, model interpretation is performed through Shapley Additive Interpretation (SHAP) analysis to clarify the importance of each feature of the model and its basis for decision-making. Finally, we chose to use the best model to develop a web-based calculator that could be used to predict the likelihood of delaying LC after PTGBD in elderly AC patients.ResultsIn multivariate logistic regression analysis, age, sex, gallbladder wall thickness, time between onset and PTGBD, white blood cell count (WBC), C-reactive protein (CRP), and neutrophil-to-lymphocyte ratio (NLR) were identified as independent predictors of delayed LC in elderly patients with AC after PTGBD. In the training set, the area under the receiver operating characteristic curve (AUC) values for these models ranged from 0.808 to 0.914, with the random forest (RF) model showing the highest AUC value. Through the evaluation of decision curve analysis (DCA), precision-recall (PR) curve and calibration curve, the RF model showed superior clinical decision support and prediction performance compared with the other seven models. Finally, we used the RF model to build an online network calculator, which aims to accurately assist doctors in making more informed and accurate clinical decisions and promote the wide application of the model in clinical practice (https://zw17786325639.shinyapps.io/Postpone/).ConclusionsThis study developed and validated an RF model network calculator based on clinical indicator information to assess the likelihood of delaying LC after PTGBD in elderly patients with acute cholecystitis. This tool is expected to assist physicians in making more appropriate clinical decisions for patients.

INTRODUCTION: Although maximizing the extent of ablation (EOA) following laser interstitial thermal therapy (LITT) can improve outcomes in patients with brain tumors, the pre-operative factors that predict a supralesional ablation (EOA >100%) remain unclear. METHODS: A retrospective study of patients with glioblastoma, metastasis, or radiation necrosis receiving LITT at a tertiary care referral center between 2013 to 2023 was conducted. Using the EOA >100% as a primary outcome, several clinical and radiographic pre-operative factors were evaluated. Five ML models, Decision Trees, Random Forest, Gradient Boosting, XGBoost, and AdaBoost, were trained using an 80/20 training/testing split and 5-fold cross validation. The models were optimized using hyperparameter tuning and evaluated against traditional logistic regression. The area under the receiver operating characteristic curve (AUC-ROC) was used to evaluate model performance. The optimal pre-operative volume cutoff for constituting an EOA >100% was also explored. RESULTS: 201 patients were included in the final cohort. Multivariate logistic regression showed that lower pre-operative volume (p < 0.001) and deep-seated lesions (p = 0.041) have a greater likelihood to undergo a supralesional ablation. Of the five ML models analyzed, Random Forest had the highest F1-score of 0.889, and XGBoost had the highest AUC-ROC of 0.74. Additionally, Random Forest (p = 0.02), Gradient Boosting (p < 0.001) and XGBoost (p = 0.01) showed statistically improved predictive performance compared to logistic regression. Features of importance were pre-operative volume, deep-seated lesions, and ellipsoid ratio. The pre-operative lesion volume cutoff for achieving an EOA >100% was 4.38 cc. CONCLUSIONS: This study shows the improved predictive ability of ML models over a traditional regression model in determining pre-operative factors for a supralesional ablation of brain tumors following LITT. Notably, the ML models identified pre-operative tumor volume, deep-seated tumors, and ellipsoid tumors as important factors, which could optimize treatment decisions and patient outcomes.

Summary for Social Media@AliciaCastongu2, @FazalZaidi9, @oozaidat, @Mouhammad_JumaaObjectiveMachine learning (ML) algorithms have emerged as powerful predictive tools in the field stroke. Here, we examine the predictive accuracy of ML models for predicting functional outcomes using 24‐hour post‐treatment characteristics in the Systematic Evaluation of Patients Treated With Neurothrombectomy Devices for Acute Ischemic Stroke (STRATIS) Registry.MethodsML models, adaptive boost, random forest (RF), classification and regression trees (CART), C5.0 decision tree (C5.0), support vector machine (SVM), least absolute shrinkage and selection operator (LASSO), and logistic regression (LR), and traditional LR models were used to predict 90‐day functional outcome (modified Rankin Scale score 0–2). Twenty‐four‐hour National Institutes of Health Stroke Scale (NIHSS) was examined as a continuous or dichotomous variable in all models. Model accuracy was assessed using the area under characteristic curve (AUC).ResultsThe 24‐hour NIHSS score was a top‐predictor of functional outcome in all models. ML models using the continuous 24‐hour NIHSS scored showed moderate‐to‐good predictive performance (range mean AUC: 0.76–0.92); however, RF (AUC: 0.92 ± 0.028) outperformed all ML models, except LASSO (AUC: 0.89 ± 0.023, p = 0.0958). Importantly, RF demonstrated a significantly higher predictive value than LR (AUC: 0.87 ± 0.031, p = 0.048) and traditional LR (AUC: 85 ± 0.06, p = 0.035) when using the 24‐hour continuous NIHSS score. Predictive accuracy was similar between the 24‐hour NIHSS score dichotomous and continuous ML models.InterpretationIn this substudy, we found similar predictive accuracy for functional outcome when using the 24‐hour NIHSS score as a continuous or dichotomous variable in ML models. ML models had moderate‐to‐good predictive accuracy, with RF outperforming LR models. External validation of these ML models is warranted. ANN NEUROL 2023;93:40–49

The accurate identification of collision positions on containers is critical in logistics and trade for enhancing cargo safety and determining accident liability. Traditional visual inspection methods are labor-intensive, time-consuming, and costly. This study leverages data from an Inertial Measurement Unit sensor and evaluates combinations of machine learning models and feature selection methods to identify the optimal approach for collision position detection. Five machine learning models (decision tree, k-nearest neighbors, support vector machine, random forest, and extreme gradient boosting) and five feature selection methods (Pearson’s correlation coefficient, mutual information, sequential forward selection, sequential backward selection, and extra trees) were assessed using three performance metrics: accuracy, execution time, and CPU utilization. Statistical analysis with the Friedman test confirmed significant differences in model and feature selection performance. The combination of k-nearest neighbors and extra trees achieved the highest accuracy of 97.1%, demonstrating that inexpensive IMU acceleration data can provide a cost-effective, efficient, and reliable solution for collision detection. This has strong practical implications for improving accident accountability and reducing inspection costs in the logistics industry.

Hyperparameter tuning and performance assessment of statistical and machine-learning algorithms using spatial data

Purpose:Machine learning classification algorithms (classifiers) forprediction of treatment response are becoming more popular in radiotherapyliterature. General Machine learning literature provides evidence in favorof some classifier families (random forest, support vector machine, gradientboosting) in terms of classification performance. The purpose of this studyis to compare such classifiers specifically for (chemo)radiotherapy datasetsand to estimate their average discriminative performance for radiationtreatment outcome prediction.Methods:We collected 12 datasets (3496 patients) from prior studies onpost-(chemo)radiotherapy toxicity, survival, or tumor control with clinical,dosimetric, or blood biomarker features from multiple institutions and fordifferent tumor sites, that is, (non-)small-cell lung cancer, head and neckcancer, and meningioma. Six common classification algorithms with built-infeature selection (decision tree, random forest, neural network, supportvector machine, elastic net logistic regression, Logit-Boost) were appliedon each dataset using the popular open-source R packagecaret. The R code and documentationfor the analysis are available online (https://github.com/timodeist/classifier_selection_code). Allclassifiers were run on each dataset in a 100-repeated nested fivefoldcross-validation with hyperparameter tuning. Performance metrics (AUC,calibration slope and intercept, accuracy, Cohen’s kappa, and Brierscore) were computed. We ranked classifiers by AUC to determine whichclassifier is likely to also perform well in future studies. We simulatedthe benefit for potential investigators to select a certain classifier for anew dataset based on our study (pre-selection based onother datasets) or estimating the best classifier for a dataset(set-specific selection based on information from thenew dataset) compared with uninformed classifier selection (randomselection).Results:Random forest (best in 6/12 datasets) and elastic net logisticregression (best in 4/12 datasets) showed the overall best discrimination,but there was no single best classifier across datasets. Both classifiershad a median AUC rank of 2. Preselection and set-specificselection yielded a significant average AUC improvement of 0.02 and 0.02over random selection with an average AUC rank improvementof 0.42 and 0.66, respectively.Conclusion:Random forest and elastic net logistic regression yield higherdiscriminative performance in (chemo)radiotherapy outcome and toxicityprediction than other studied classifiers. Thus, one of these twoclassifiers should be the first choice for investigators when buildingclassification models or to benchmark one’s own modeling resultsagainst. Our results also show that an informed preselection of classifiersbased on existing datasets can improve discrimination over randomselection.

Bias-reduced dental age estimation in children: Machine-learning evaluation and a novel constrained random forest adaptation of the Cameriere method.

BackgroundMachine learning (ML) has shown great potential in recognizing complex disease patterns and supporting clinical decision-making. Diabetic foot ulcers (DFUs) represent a significant multifactorial medical problem with high incidence and severe outcomes, providing an ideal example for a comprehensive framework that encompasses all essential steps for implementing ML in a clinically relevant fashion.ObjectiveThis paper aims to provide a framework for the proper use of ML algorithms to predict clinical outcomes of multifactorial diseases and their treatments.MethodsThe comparison of ML models was performed on a DFU dataset. The selection of patient characteristics associated with wound healing was based on outcomes of statistical tests, that is, ANOVA and chi-square test, and validated on expert recommendations. Imputation and balancing of patient records were performed with MIDAS (Multiple Imputation with Denoising Autoencoders) Touch and adaptive synthetic sampling, respectively. Logistic regression, support vector machine (SVM), k-nearest neighbors, random forest (RF), extreme gradient boosting (XGBoost), Bayesian additive regression trees, and artificial neural network were trained, cross-validated, and optimized using random sampling on the patient dataset. To evaluate model calibration and clinical utility, calibration curves, Brier scores, and decision curve analysis (DCA) were performed.ResultsThe exploratory dataset consisted of 700 patient records with 199 variables. After dataset cleaning, the variables used for model training included age, smoking status, toe systolic pressure, blood pressure, oxygen saturation, hemoglobin, hemoglobin A1c, estimated glomerular filtration rate, wound location, diabetes type, Texas wound classification, neuropathy, and wound area measurement. The SVM obtained a stable accuracy of 0.853 (95% CI 0.810-0.896) with an area under the receiver operating characteristic curve of 0.922 (95% CI 0.889-0.955). The RF and XGBoost acquired an accuracy of 0.838 (95% CI 0.793-0.883) and 0.815 (95% CI 0.768-0.862), respectively, with areas under the receiver operating characteristic curve of 0.917 (95% CI 0.883-0.951) for RF and 0.889 (95% CI 0.849-0.929) for XGBoost. SVM, RF, and XGBoost were well-calibrated, with average Brier scores around 0.127 (SD 0.013). DCA showed that the SVM provided the highest net clinical benefit across relevant risk thresholds.ConclusionsHandling missing values, feature selection, and addressing class imbalance are critical components of the key steps in developing ML applications for clinical research. Seven models were selected for comparing their predictive power regarding complete wound healing, and each model representing a different branch in ML. In this initial DFU dataset used as an example, the SVM achieved the best performance in predicting clinical outcomes, followed by RF and XGBoost. The model’s calibration and clinical utility were determined through calibration curves, Brier scores, and DCA, demonstrating its potential relevance in clinical decision-making.