Applying data mining techniques to classify patients with suspected hepatitis C virus infection

Machine learning classifiers are increasingly used to create predictive models for pathological complete response (pCR) in breast cancer after neoadjuvant therapy (NAT). Few studies have compared the effectiveness of different ML classifiers. This study evaluated radiomics models based on pre- and post-contrast first-phase T1 weighted images (T1WI) in predicting breast cancer pCR after NAT and compared the performance of ML classifiers. This retrospective study enrolled 281 patients undergoing NAT from the Duke-Breast-Cancer-MRI dataset. Radiomic features were extracted from pre- and post-contrast first-phase T1WI images. The Synthetic Minority Oversampling Technique (SMOTE) was applied, then the dataset was randomly divided into training and validation groups (7:3). The radiomics model was built using selected optimal features. Support vector machine (SVM), random forest (RF), decision tree (DT), k-nearest neighbor (KNN), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM) were classifiers. Receiver operating characteristic curves were used to assess predictive performance. LightGBM performed best in predicting pCR [area under the curve (AUC): 0.823, 95% confidence interval (CI) [0.743-0.902], accuracy 74.0%, sensitivity 85.0%, specificity 67.2%]. During subgroup analysis, RF was most effective in pCR prediction in luminal breast cancers (AUC: 0.914, 95% CI [0.847-0.981], accuracy 87.0%, sensitivity 85.2%, specificity 88.1%). In triple-negative breast cancers, LightGBM performed best (AUC: 0.836, 95% CI [0.708-0.965], accuracy 78.6%, sensitivity 68.2%, specificity 90.0%). The LightGBM-based radiomics model performed best in predicting pCR in patients with breast cancer. RF and LightGBM showed promising results for luminal and triple-negative breast cancers, respectively.

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.

Postoperative pain is a frequent clinical concern following endodontic treatment. This study aimed to develop and validate supervised machine learning models to predict the occurrence of postoperative pain in cases of irreversible pulpitis. A prospective sample of 354 patients aged 18 to 60 years undergoing standardised endodontic treatment was analysed. In the original randomised clinical trials from which the data were derived, each patient had only one eligible tooth included. Clinical variables included postoperative pain at 24 and 72 h, treated tooth group, occlusal reduction, photobiomodulation therapy, use of non-steroidal anti-inflammatory drugs (NSAIDs), sex and age. Eight supervised machine learning algorithms were trained to predict pain occurrence, including Logistic Regression, Support Vector Machine, Gradient Boosting, Random Forest, Decision Tree, K-Nearest Neighbours, AdaBoost and Multilayer Perceptron. The dataset was divided into training (70%) and testing (30%) sets using stratified sampling. Class imbalance in the training set, characterised by a lower proportion of cases with moderate or severe pain, was addressed using the Synthetic Minority Oversampling Technique. Hyperparameters were optimised through grid search combined with stratified five-fold cross validation. Model performance was evaluated using the area under the curve (AUC), accuracy, precision, recall and F1-score, with 95% confidence intervals estimated by bootstrapping. The predictive models achieved good discrimination of pain outcomes. Logistic Regression showed the best test performance at 24 h (AUC 0.74 [95% CI: 0.61 to 0.85], precision 0.81 [95% CI: 0.73 to 0.88]). At 72 h, the Support Vector Machine achieved the highest performance (AUC 0.81 [95% CI: 0.69 to 0.92], precision 0.88 [95% CI: 0.79 to 0.94]). Age and sex emerged as the most influential predictors across models. Supervised machine learning models demonstrated promising performance for predicting postoperative pain following endodontic treatment. Logistic Regression and Support Vector Machine algorithms presented the most consistent results, supporting their potential clinical application for personalised pain management.

ObjectiveTo develop and validate a generalized prediction model that can classify epidermal growth factor receptor (EGFR) mutation status in non–small cell lung cancer patients.MethodsA total of 346 patients (296 in the training cohort and 50 in the validation cohort) from four centers were included in this retrospective study. First, 1085 features were extracted using IBEX from the computed tomography images. The features were screened using the intraclass correlation coefficient, hypothesis tests and least absolute shrinkage and selection operator. Logistic regression (LR), decision tree (DT), random forest (RF), and support vector machine (SVM) were used to build a radiomics model for classification. The models were evaluated using the following metrics: area under the curve (AUC), calibration curve (CAL), decision curve analysis (DCA), concordance index (C-index), and Brier score.ResultsSixteen features were selected, and models were built using LR, DT, RF, and SVM. In the training cohort, the AUCs was .723, .842, .995, and .883; In the validation cohort, the AUCs were .658, 0567, .88, and .765. RF model with the best AUC, its CAL, C-index (training cohort=.998; validation cohort=.883), and Brier score (training cohort=.007; validation cohort=0.137) showed a satisfactory predictive accuracy; DCA indicated that the RF model has better clinical application value.ConclusionMachine learning models based on computed tomography images can be used to evaluate EGFR status in patients with non–small cell lung cancer, and the RF model outperformed LR, DT, and SVM.

Lung cancer remains one of the leading causes of cancer-related mortality worldwide, where early risk identification is critical for improving survival outcomes. While existing machine learning approaches for lung cancer prediction frequently rely on medical imaging, such methods are costly and often impractical in low-resource clinical settings. This study proposes an efficient and interpretable lung cancer risk prediction framework using demographic, lifestyle, and symptom-based data. A Genetic Algorithm (GA) is employed as a metaheuristic optimization strategy to jointly perform feature selection and hyperparameter tuning of a Random Forest (RF) classifier. To address the inherent class imbalance in the dataset, the Synthetic Minority Oversampling Technique (SMOTE) is applied exclusively to the training data to prevent information leakage and enhance minority-class learning. The proposed GA-optimized RF model is evaluated against several baseline classifiers, including Logistic Regression, Support Vector Machine, k-Nearest Neighbors, Decision Tree, standard Random Forest, and XGBoost, using accuracy, precision, recall, F1-score, and ROCAUC as evaluation metrics. Experimental results demonstrate that the optimized RF model achieves superior performance, with an accuracy of 90.6%, an F1-score of 0.885, and a ROC-AUC of 0.917, outperforming all baseline models. Feature importance analysis identifies smoking habit, breathing difficulties, and throat discomfort as the most influential predictors, aligning with established clinical knowledge. The findings highlight that a metaheuristic-driven optimization approach applied to non-imaging data can provide a cost-effective, reliable, and interpretable solution for early lung cancer risk screening, particularly in resource-constrained healthcare environments.

BackgroundCranial, facial, nasal, and maxillary widths have been shown to be significantly affected by the individual’s sex. The present study aims to use measurements of dental arch and maxillary skeletal base to determine sex, employing supervised machine learning.Materials and methodsMaxillary and mandibular tomographic examinations from 100 patients were analyzed to investigate the inter-premolar width, inter-molar width, maxillary width, inter-pterygoid width, nasal cavity width, nostril width, and maxillary length, obtained through Cone Beam Computed Tomography scans. The following machine learning algorithms were used to build the predictive models: Logistic Regression, Gradient Boosting Classifier, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Multi-Layer Perceptron Classifier (MLP), Decision Tree, and Random Forest Classifier. A 10-fold cross-validation approach was adopted to validate each model. Metrics such as area under the curve (AUC), accuracy, recall, precision, and F1 Score were calculated for each model, and Receiver Operating Characteristic (ROC) curves were constructed.ResultsUnivariate analysis showed statistical significance (p < 0.10) for all skeletal and dental variables. Nostril width showed greater importance in two models, while Inter-molar width stood out among dental measurements. The models achieved accuracy values ranging from 0.75 to 0.85 on the test data. Logistic Regression, Random Forest, Decision Tree, and SVM models had the highest AUC values, with SVM showing the smallest disparity between cross-validation and test data for accuracy metrics.ConclusionTransverse dental arch and maxillary skeletal base measurements exhibited strong predictive capability, achieving high accuracy with machine learning methods. Among the evaluated models, the SVM algorithm exhibited the best performance. This indicates potential usefulness in forensic sex determination.

Metabolomic profiling and machine learning application for discriminating fatal hypothermia in a rat model.

BackgroundA considerable portion of patients with fever of unknown origin (FUO) present concomitant lymphadenopathy. Diseases within the spectrum of FUO accompanied by lymphadenopathy include lymphoma, infections, and rheumatic diseases. Particularly, lymphoma has emerged as the most prevalent etiology of FUO with associated lymphadenopathy. Distinguishing between benign and malignant lymph node lesions is a major challenge for physicians and an urgent clinical concern for patients. However, conventional imaging techniques, including PET/CT, often have difficulty accurately distinguishing between malignant and benign lymph node lesions. This study utilizes PET/CT radiomics to differentiate between lymphoma and benign lymph node lesions in patients with FUO, aiming to improve diagnostic accuracy.ResultsData were collected from 204 patients who underwent 18F-FDG PET/CT examinations for FUO, including 114 lymphoma patients and 90 patients with benign lymph node lesions. Patients were randomly divided into training and testing groups at a ratio of 7:3. A total of 15 effective features were obtained by the least absolute shrinkage and selection operator (LASSO) algorithm. Machine learning models were constructed using logistic regression (LR), support vector machine (SVM), random forest (RF), and k-nearest neighbors (KNN) algorithms. In the training group, the area under the curve (AUC) values for predicting lymphoma and benign cases by LR, SVM, RF, and KNN models were 0.936, 0.930, 0.998, and 0.938, respectively. There were statistically significant differences in AUC between the RF and other models (all P < 0.001). In the testing group, the AUC values for the four models were 0.860, 0.866, 0.915, and 0.891, respectively, with no statistically significant differences observed among them (all P > 0.05). The decision curve analysis (DCA) curves of the RF model outperformed those of the other three models in both the training and testing groups.ConclusionsPET/CT radiomics demonstrated promising performance in discriminating lymphoma from benign lymph node lesions in patients with FUO, with the RF model showing the best performance.

Three machine learning classifiers: random forest, decision tree and support vector machine were used to build predictive models of an anti-mycobacterial ChEMBL database and evaluated for their predictive capability. Before the development of predictive models, data pre-processing was carried out to fix the class imbalance problem by applying cost-sensitive classifier, and filtration of data instance by supervised synthetic minority oversampling technique (SMOTE), spread subsample and resample method. The statistical evaluation indicated that random forest model was the best model as it showed the best accuracy 93.83%, specificity 90.5%, receiver operating characteristic (ROC) 0.984, MCC 0.772 and kappa statistics 0.768 in comparison to other models whereas LibSVM showed the highest sensitivity 94.4% compared with others. Additionally, toxicity predictive models based on SingleCellcall DSSTox carcinogenicity database (AID1189) was developed which resulted in random forest model as the best model. The deployment of both RF predictive models on two unknown datasets resulted in 1317 compounds out of 1554 approved drugs and 2234 compounds out of 18,746 ChEMBL anti-malarial dataset as non-toxic and anti-mycobacterial compounds. Thus machine learning models present highly efficient methods to find out novel hit anti-mycobacterial compounds. We suggest that such machine learning techniques could be very useful to screen drug candidates not only for tuberculosis but also for other diseases.

A method for accurately classifying donkey breeds has been developed by integrating single nucleotide polymorphism (SNPs) data with machine learning algorithms. The approach includes preprocessing donkey genomic sequencing data, addressing data imbalance with the Synthetic Minority Over-sampling Technique (SMOTE), and utilizing an improved Leave-One-Out Cross-Validation (LOOCV) for dataset partitioning. Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest (RF) models were constructed and evaluated. The results demonstrated that different chromosomes significantly influence classifier performance. For instance, chromosome Chr2 showed the highest classification accuracy with KNN, while chromosome Chr19 performed best with SVM and RF models. After enhancing data quality and addressing imbalances, classification performance improved substantially, with accuracy, precision, recall, and F1 score showing increases of up to 15% in certain models, particularly on key chromosomes. This method offers an effective solution for donkey breed classification and provides technical support for the conservation and development of donkey genetic resources.

To predict the gamma passing rate (GPR) in dosimetric verification of intensity-modulated radiotherapy (IMRT) using three machine learning models based on plan complexity and find the best prediction model by comparing and evaluating the prediction ability of the regression and classification models of three classical algorithms: artificial neural network (ANN), support vector machine (SVM) and random forest (RF). 269 clinical IMRT plans were chosen retrospectively and the GPRs of a total of 2340 fields by the 2%/2mm standard at the threshold of 10% were collected for dosimetric verification using electronic portal imaging device (EPID). Subsequently, the plan complexity feature values of each field were extracted and calculated, and a total of 6 machine learning models (classification and regression models for three algorithms) were trained to learn the relation between 21 plan complexity features and GPRs. Each model was optimized by tuning the hyperparameters and ten-fold cross validation. Finally, the GPRs predicted by the model were compared with measured values to verify the accuracy of the model, and the evaluation indicators were applied to evaluate each model to find the algorithm with the best prediction performance. The RF algorithm had the optimal prediction effect on GPR, and its mean absolute error (MAE) on the test set was 1.81%, root mean squared error (RMSE) was 2.14%, and correlation coefficient (CC) was 0.72; SVM was the second and ANN was the worst. Among the classification models, the RF algorithm also had the optimal prediction performance with the highest area under the curve (AUC) value of 0.80, specificity and sensitivity of 0.80 and 0.68 respectively, followed by SVM and the worst ANN. All the three classic algorithms, ANN, SVM, and RF, could realize the prediction and classification of GPR. The RF model based on plan complexity had the optimal prediction performance which could save valuable time for quality control workers to improve quality control efficiency.

Customer turnover is a crucial issue in banking since maintained profitability depends on keeping clients. This work aims to categorize consumer turnover in banks by using a new ensemble approach combining many machine learning methods, hence enhancing churn prediction models. Using a comprehensive dataset including demographic, financial, and behavioral data—such as credit score, account balance, tenure, and activity levels—the study employs the goal variable revealing if a customer has left the bank. The study starts with univariate, bivariate, and multivariate feature exploration and subsequently uses the Interquartile Range (IQR) approach to identify outliers thereby improving the data quality. Five models—K-Nearest Neighbors, Support Vector Classifier, Random Forest, Decision Tree, and XGBoost—a Voting Classifier ensemble—are used to estimate project churn. Building upon all the strengths of each model, this approach improves the prediction of classification and provides a balanced and highly robust classification system. The applied approaches are K-Nearest Neighbors (KNN), Support Vector Classifier (SVC), Random Forest, Decision Tree, and XGBoost within a Voting Classifier configuration. The performance of the Voting Classifier without SMOTE yields the following results: Accuracy: 0.87, precision: 0.87, recall: 0.80, and F1-Score: 0.87. The proposed model that extend the base model using SMOTE (Synthetic Minority Over-sampling Technique), yields a higher prediction accuracy of 0.90, precision of 0.90, recall of 0.90 and F1-Score of 0.90. This enhancement is proving the efficiency of SMOTE to handle the class imbalance problem in order to render the churn prediction more balanced and reliable system. The proposed approach assures a reliable solution to the strategies to retain the customers in the banking organisations.

<strong class="journal-contentHeaderColor">Abstract.</strong> This study aims to identify key fire factors via recursive feature elimination (RFE) to generate a forest fire susceptibility map (FSM) using support vector machine (SVM), and random forest (RF) models. The fire zones were derived from MODIS satellite imagery from 2012 to 2017. Further validation of these data has been provided by field surveys and reviews of land records in rangelands and forests; a total of 352 fire points were determined in this study. Seventeen factors involving topography, geomorphology, meteorology, hydrology, and anthropology were identified as being effective primary factors in triggering and spreading fires in the selected mountainous case study area. As a first step, the RFE models of the RF, Extra trees, Gradient boosting, and AdaBoost was used to identify important fire factors among all selected primary factors. The SVM and RF models were applied once on all factors and the second on those derived from RFE models as the key factors in FSM. Training and testing data were divided tenfold, and the model's performance was evaluated using cross-validation (CV). Different metrics were used to measure accuracy, including recall, precision, F1, accuracy, the area under the curve (AUC), Matthews correlation coefficient (MCC), and Kappa. The accuracy assessment process shows that the FSM results are further improved by leveraging RFE models to distinguish the key factors and not include unnecessary factors. The greatest improvement is for SVM, with more than 10.97 % and 8.61 % in the accuracy and AUC metrics, respectively.

ObjectivesTo develop and to propose a machine learning model for predicting glaucoma and identifying its risk factors.MethodData analysis pipeline is designed for this study based on Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology. The main steps of the pipeline include data sampling, preprocessing, classification and evaluation and validation. Data sampling for providing the training dataset was performed with balanced sampling based on over-sampling and under-sampling methods. Data preprocessing steps were missing value imputation and normalization. For classification step, several machine learning models were designed for predicting glaucoma including Decision Trees (DTs), K-Nearest Neighbors (K-NN), Support Vector Machines (SVM), Random Forests (RFs), Extra Trees (ETs) and Bagging Ensemble methods. Moreover, in the classification step, a novel stacking ensemble model is designed and proposed using the superior classifiers.ResultsThe data were from Shahroud Eye Cohort Study including demographic and ophthalmology data for 5190 participants aged 40-64 living in Shahroud, northeast Iran. The main variables considered in this dataset were 67 demographics, ophthalmologic, optometric, perimetry, and biometry features for 4561 people, including 4474 non-glaucoma participants and 87 glaucoma patients. Experimental results show that DTs and RFs trained based on under-sampling of the training dataset have superior performance for predicting glaucoma than the compared single classifiers and bagging ensemble methods with the average accuracy of 87.61 and 88.87, the sensitivity of 73.80 and 72.35, specificity of 87.88 and 89.10 and area under the curve (AUC) of 91.04 and 94.53, respectively. The proposed stacking ensemble has an average accuracy of 83.56, a sensitivity of 82.21, a specificity of 81.32, and an AUC of 88.54.ConclusionsIn this study, a machine learning model is proposed and developed to predict glaucoma disease among persons aged 40-64. Top predictors in this study considered features for discriminating and predicting non-glaucoma persons from glaucoma patients include the number of the visual field detect on perimetry, vertical cup to disk ratio, white to white diameter, systolic blood pressure, pupil barycenter on Y coordinate, age, and axial length.

&lt;sec&gt; &lt;title&gt;Purpose:&lt;/title&gt; This study aimed to identify severe Coronavirus Disease 2019 (COVID-19) cases combining blood test results and imaging parameters based on a machine learning classifier at the initial admission. &lt;/sec&gt; &lt;sec&gt; &lt;title&gt;Materials and methods:&lt;/title&gt; Ninety-five non-severe and 22 severe laboratory-confirmed COVID-19 cases treated between January 23, 2020 and March 25, 2020 were examined in this retrospective trial. Blood test results and chest computed tomography (CT) images were obtained at the initial admission. The lesions on CT images were segmented using an artificial intelligent (AI) tool. Then, quantitative CT (QCT) parameters, including the volume, percentage, ground glass opacity (GGO) percentage and heterogeneity of the lesions were calculated. Correlations of blood test results and QCT parameters were analyzed by the Pearson test first. Then, discriminative features for detecting severe cases were selected by both the independent samples t test and least absolute shrinkage and selection operator (LASSO) regression. Next, support vector machine (SVM), Gaussian naïve Bayes (GNB), Knearest neighbor (KNN), decision tree (DT), random forest (RF) and multi-layer perceptron-neural net (MLP-NN) algorithms were used as classifiers, and their accuracies were assessed by 10-fold-cross-validation. &lt;/sec&gt; &lt;sec&gt; &lt;title&gt;Results:&lt;/title&gt; Blood test indexes and CT parameters were moderately to medially correlated. Of all selected features, lesion percentage contributed mostly to the classification of the two groups, followed by lesion volume, patient age, lymphocyte count, neutrophil count, GGO percentage and tumor heterogeneity. RF-assisted identification had the highest accuracy of 91.38%, followed by GNB (87.83%), KNN (87.93%), SVM (86.21%), MLP-NN (85.34%) and DT (84.48%). &lt;/sec&gt; &lt;sec&gt; &lt;title&gt;Conclusions:&lt;/title&gt; The RF-assisted model combining blood test and QCT parameters is helpful in the identification of severe COVID-19 cases. &lt;/sec&gt;