AI-Optimized Ensemble Model with Hyperparameter Tuning for Brain Tumor Detection

Anemia is one of the common adverse reactions after hip fracture surgery. The traditional method to solve anemia is allogeneic transfusion. However, the transfusion may lead to some complications such as septicemia and fever. So far, few studies have reported roles of machine learning in predicting whether blood transfusion is needed or not after hip fracture surgery. Therefore, the purpose of this study is to develop machine learning models to predict the likelihood of postoperative blood transfusion in patients undergoing hip fracture surgery. This study enrolled 1355 patients who underwent hip fracture surgery at the Affiliated Hospital of Qingdao University from January 2016 to December 2021. Among all patients, 210 cases received postoperative blood transfusion. All patients were randomly divided into a training group and a testing group at a ratio of 7:3. In the training group, univariate and multivariate logistic regression analyses were used to determine independent risk factors for the postoperative transfusion. Then, based on these independent risk factors, tenfold cross-validation method was utilized to develop five machine learning models, including logistic, multilayer perceptron (MLP), extreme gradient boosting (XGBoost), random forest (RF), and support vector machine (SVM). The receiver operating characteristic (ROC) curve, area under ROC curve (AUC), and Matthews correlation coefficient (MCC) were generated to evaluate the performance of the models. Calibration plot and decision curve analysis (DCA) were used to test the performance, stability, and clinical applicability of the models. The models were validated using the testing group; and the ROC curve, MCC, calibration plot, and DCA curves were also generated to validate the performance, stability, and clinical applicability of the models. To further verify the robustness of the model, we randomly grabbed 70% of the samples in the testing set, performed 1000 iterations, and calculated the AUC and confidence interval of the five models. Finally, we used SHapley Additive exPlanations (SHAP) to explain these models. Multivariate logistic regression analysis showed that there were 8 independent risk factors, including age, blood transfusion history, albumin (ALB), globulin (GLO), total bilirubin (TBIL), indirect bilirubin (IBIL), hemoglobin (HB), and blood loss > 200ml. We finally selected five independent risk factors including HB, GLO, age, IBIL, and blood loss > 200ml. Based on these five independent risk factors, we generated six characteristic variables, namely HB, HB × HB, HB × blood loss, GLO × HB, age, age × IBIL, and established five machine learning models using a tenfold cross-validation method. In the training group, the AUC values of logistic, RF, MLP, SVM, and XGB were 0.9320, 0.8911, 0.9327, 0.9225, and 0.8825, respectively, and the average AUC was 0.9122 ± 0.0212. The MCC values were 0.65, 0.77, 0.65, 0.66, and 0.68, respectively, and the calibration plot and DCA performed well. In the testing group the AUC values of logistic, RF, MLP, SVM, and XGB were 0.8483, 0.7978, 0.8576, 0.8598, and 0.8216, respectively. The average AUC was 0.8370 ± 0.0238, and the MCC values were 0.41, 0.35, 0.40, 0.41, and 0.41, respectively. The calibration plot and DCA in the testing group also showed good performance. The AUC values and confidence intervals of the 1000-iteration model were: logistic (AUC, min confidence interval [CI]-max confidence interval [CI] 0.848, 0.804-0.903), RF (AUC, minCI-maxCI 0.797, 0.734-0.857), MLP (AUC, minCI-maxCI 0.858, 0.812-0.902), SVM (AUC, minCI-maxCI 0.859, 0.819-0.910), and XGB (AUC, minCI-maxCI 0.821, 0.764-0.894). The model performed well. Finally, according to SHAP, among all five models, HB played the most important role in model prediction and interpretation. The five models we developed all performed well in predicting the likelihood of blood transfusion after hip fracture surgery. Therefore, we believed that the prediction model based on machine learning had great application prospects in clinical practice, which could help clinicians better predict the risk of blood transfusion after hip fracture surgery.

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

Mental health remains one of the most challenging public health concerns in the world, particularly in a developing country like Bangladesh. The objective of this study is to determine the key determinants of depression among ever-married women in Bangladesh. This study used 19,987 ever-married women aged 15-49 years in Bangladesh, from the 2022 Bangladesh Demographic and Health Survey (BDHS). The key features selection was selected by using Boruta algorithm. Total seven machine learning approaches, including Extreme Gradient Boosting (XGBoost), Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), Logistic Regression (LR), K-Nearest Neighbour’s (KNN), and Neural Network (NN) were evaluated for predictive performance. All models were highly tuned with 5-fold cross-validation and tested on several complementary predictions including accuracy, precision, recall, F1 score, Matthews Correlation Coefficient (MCC), Cohen's Kappa, and Area Under the Receiver Operating Characteristic Curve (AUROC). The top-performing algorithm was observed XGBoost approach with the highest AUROC of 0.799, indicating strong discriminatory power between non-depressed and depressed women. Results from XGBoost approach revealed that anxiety, currently working, age group 45-49 years, internet user, menstruated last 6 weeks, pressure to pregnant, power of decision making etc. are most important determinants of the depression of married women in Bangladesh. International Journal of Statistical Sciences, Vol. 25(2), November, 2025, pp 33-46

Breast cancer continues to be a leading cause of death among women in the world. The prediction of survival outcomes based on treatment modalities, i.e., chemotherapy, hormone therapy, surgery, and radiation therapy is an essential step towards personalization in treatment planning. However, Machine Learning (ML) models may improve these predictions by investigating intricate relationships between clinical variables and survival. This study investigates the performance of several ML models to predict survival rate in patients undergoing diverse breast cancer treatments i.e., chemotherapy, hormone therapy, surgery and radiation using multiple clinical parameters. The dataset consisted of 5000 samples and turned into downloaded from Kaggle. The models assessed blanketed Support Vector Machines (SVM), K-Nearest Neighbor (KNN), AdaBoost, Gradient Boosting, Random Forest, Gaussian Naive Bayes, Logistic Regression, Extreme Gradient Boosting (XG boost), and Decision tree. Performance of the models was assessed using parameters such as Accuracy, Precision, Recall, F1-Score and Area under the Receiver Operating Characteristic Curve (AUC-ROC). SHAP (SHapley Additive exPlanations) values analysis was done to provide an explanation for the impact of a feature on model predictions using Waterfall and Beeswarm plots. Anticipated baseline (E(f(x))) were in comparison to the predictions (f(x)) for each therapy group. Performance of Gradient Boosting was shown to be the best with an Accuracy: 0.972, Precision: 0.973, Recall: 0.972, F1-Score: 0.973, and AUC-ROC Score: 0.997. Chemotherapy had a notably bad impact on survival, with an f(x) of -0.274 and an E(f(x)) of -0.025. Hormone therapy showed the maximum detrimental effect on survival, with an f(x) of -0.408. Surgical operation had an extraordinarily impartial impact (f(x) = -0.041), even as radiation therapy positively impacted survival results with an f(x) of 0.22. Gradient Boosting was the most predictive algorithm for breast cancer survival outcomes. This SHAP-primarily based analysis provides a complete knowledge of ways one-of-a-kind treatments have an effect on survival predictions in breast cancer patients. Radiation therapy indicates the most tremendous effect on survival, whilst hormone therapy reveals the maximum poor effect. Future studies need to explore personalized treatment strategies that comprise these insights to enhance patient effects.

Background and aimsThe birth weight of a newborn is a crucial factor that affects their overall health and future well-being. Low birth weight (LBW) is a widespread global issue, which the World Health Organization defines as weighing less than 2,500 g. LBW can have severe negative consequences on an individual’s health, including neonatal mortality and various health concerns throughout their life. To address this problem, this study has been conducted using BDHS 2017–2018 data to uncover important aspects of LBW using a variety of machine learning (ML) approaches and to determine the best feature selection technique and best predictive ML model.MethodsTo pick out the key features, the Boruta algorithm and wrapper method were used. Logistic Regression (LR) used as traditional method and several machine learning classifiers were then used, including, DT (Decision Tree), SVM (Support Vector Machine), NB (Naïve Bayes), RF (Random Forest), XGBoost (eXtreme Gradient Boosting), and AdaBoost (Adaptive Boosting), to determine the best model for predicting LBW. The model’s performance was evaluated based on the specificity, sensitivity, accuracy, F1 score and AUC value.ResultsResult shows, Boruta algorithm identifies eleven significant features including respondent’s age, highest education level, educational attainment, wealth index, age at first birth, weight, height, BMI, age at first sexual intercourse, birth order number, and whether the child is a twin. Incorporating Boruta algorithm’s significant features, the performance of traditional LR and ML methods including DT, SVM, NB, RF, XGBoost, and AB were evaluated where LR, had a specificity, sensitivity, accuracy and F1 score of 0.85, 0.5, 85.15% and 0.915. While the ML methods DT, SVM, NB, RF, XGBoost, and AB model’s respective accuracy values were 85.35%, 85.15%, 84.54%, 81.18%, and 84.41%. Based on the specificity, sensitivity, accuracy, F1 score and AUC, RF (specificity = 0.99, sensitivity = 0.58, accuracy = 85.86%, F1 score = 0.9243, AUC = 0.549) outperformed the other methods. Both the classical (LR) and machine learning (ML) models’ performance has improved dramatically when important characteristics are extracted using the wrapper method. The LR method identified five significant features with a specificity, sensitivity, accuracy and F1 score of 0.87, 0.33, 87.12% and 0.9309. The region, whether the infant is a twin, and cesarean delivery were the three key features discovered by the DT and RF models, which were implemented using the wrapper technique. All three models had the identical F1 score of 0.9318. However, “child is twin” was recognized as a significant feature by the SVM, NB, and AB models, with an F1 score of 0.9315. Ultimately, with an F1 score of 0.9315, the XGBoost model recognized “child is twin” and “age at first sex” as relevant features. Random Forest again beat the other approaches in this instance.ConclusionsThe study reveals Wrapper method as the optimal feature selection technique. The ML method outperforms traditional methods, with Random Forest (RF) being the most effective predictive model for Low-Birth-Weight prediction. The study suggests that policymakers in Bangladesh can mitigate low birth weight newborns by considering identified risk factors.

Gender classification is one of the major challenges in the domain of speech analysis now-a-days. Identification of gender from acoustic properties of voice such as mean, median, and frequency, is of high importance. Machine learning is used to solve this problem because it gives promising results for classification techniques. There are several algorithms that can be used to predict the gender using acoustic properties. In this work, classifiers are evaluated using 6 different machine learning algorithms. These algorithms include K-Nearest Neighbor (KNN), Decision Tree (DT), Random Forest (RF), and Support Vector Machine (SVM), Support Vector Machine Using Poly kernel and Gradient Boosting (GB). For the same voice sample dataset, the accuracy, recall, precision and F1 Score are computed for all the above-mentioned classifiers. The accuracy obtained from KNN is 96.71, DT is 93.33%, Random Forest is 97.72%, SVM is 98.48%, SVM using Poly Kernel is 97.72%, Gradient Boosting is 96.96%. The predicted values of the considered evaluation metrices on test data for all above classifiers evidenced the supremacy of SVM classifier as it is best fitted model for gender identification of acoustic. SVM attained the accuracy of 98.48% which is the highest among all.

This study applies machine learning (ML) techniques to classify fertile [for porphyry Cu and (or) Au systems] and barren adakites using geochemical data from New Brunswick, Canada. It emphasizes that not all intrusive units, including adakites, are inherently fertile and should not be directly used as the heat source evidence layer in mineral prospectivity mapping without prior analysis. Adakites play a crucial role in mineral exploration by helping distinguish between fertile and barren intrusive units, which significantly influence ore-forming processes. A dataset of 99 fertile and 66 barren adakites was analyzed using seven ML models: support vector machine (SVM), neural network, random forest (RF), decision tree, AdaBoost, gradient boosting, and logistic regression. These models were applied to classify 829 adakite samples from around the world into fertile and barren categories, with performance evaluated using area under the curve (AUC), classification accuracy, F1 score, precision, recall, and Matthews correlation coefficient (MCC). SVM achieved the highest performance (AUC = 0.91), followed by gradient boosting (0.90) and RF (0.89). For model validation, 160 globally recognized fertile adakites were selected from the dataset based on well-documented fertility characteristics. Among the tested models, SVM demonstrated the highest classification accuracy (93.75%), underscoring its effectiveness in distinguishing fertile from barren adakites for mineral prospectivity mapping. Statistical analysis and feature selection identified middle rare earth elements (REEs), including Gd and Dy, with Hf, as key indicators of fertility. A comprehensive analysis of 1596 scatter plots, generated from 57 geochemical variables, was conducted using linear discriminant analysis (LDA) to determine the most effective variable pairs for distinguishing fertile and barren adakites. The most informative scatter plots featured element vs. element combinations (e.g., Ga vs. Dy, Ga vs. Gd, and Pr vs. Gd), followed by element vs. major oxide (e.g., Fe2O3T vs. Gd and Al2O3 vs. Hf) and ratio vs. element (e.g., La/Sm vs. Gd, Rb/Sr vs. Hf) plots, whereas major oxide vs. major oxide, ratio vs. ratio, and major oxide vs. ratio plots had limited discriminatory power.

As the reliance on digital networks grows, the need for robust security measures becomes paramount. It offers a thorough analysis of creating and assessing a Network Intrusion Detection System (NIDS) incorporating various machine learning algorithms. The selected algorithms consist of K Nearest Neighbour, Naïve Bayes, Decision Tree, Support Vector Machine, and Logistic Regression. Moreover, widely recognised ensemble learning models like CatBoost, AdaBoost, Gradient Boost, Random Forest, and Extreme Gradient Boost. The core objective is to improve the efficiency of intrusion detection by evaluating various machine-learning models. Utilizes feature selection techniques based on chi-square to enhance the relevance and effectiveness of the selected features. The evaluation metrics, particularly Matthews’ Correlation Coefficient (MCC) and ROC Curves, are used in assessing the models’ efficacy. They offer valuable insights into the models’ capability to differentiate between normal and malicious network activities. This study aims to advance cybersecurity by comparing classical machine learning and ensemble models for network intrusion detection. Significantly, our results underscore XGBoost’s effectiveness with 0.996 accuracy and 0.993 MCC, in enhancing digital network security.

Depression and anxiety are significant global public health, their comorbidity share similar clinical symptoms with heterogeneous progression patterns for patients. The early and accurate prediction of anxiety, depression, and comorbidity of anxiety and depression is essential for timely discrimination of high-risk patients, individual treatment strategies, and hierarchical management. This study aimed to develop and validate an interpretable, multi-task prediction model using heart rate variability (HRV) for discriminating anxiety alone, depression alone, and comorbid patients with psychiatric disorders. Patients diagnosed with anxiety or depression by trained psychiatrists were retrospectively enrolled from the Department of Psychiatry, the Affiliated Hospital of Southwest Medical University, between March 2024 and April 2025. The Synthetic Minority Over-sampling Technique (SMOTE) was applied to address class imbalance, and the Recursive feature elimination (RFE) was used for feature selection. Six machine-learning algorithms, including logistic regression (LR), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), k-nearest neighbor (KNN), and naïve Bayes (NB), were adopt to develop multi-task prediction models for discriminating psychiatric comorbidities. Model performances were evaluated by receiver operating characteristic (ROC) curves, accuracy, sensitivity, specificity, F1 score, and Matthews correlation coefficient (MCC). The SHapley Additive exPlanation (SHAP) method was used to visualize the feature contributions and interpret the best model. A total of 546 patients, 114 with anxiety group(20.88%), 100 with depression group (18.32%), and 332 with comorbidity of anxiety and depression (60.81%), were included in this study. The ML-based models were developed for each task using the eleven contributing features. Among the six ML models, the XGBoost achieved the best performance across all tasks. In the validation set, this model achieved the AUCs of 0.8341 (95% confidence interval [CI]:0.761–0.907) for the task of anxiety versus comorbidity of anxiety and depression, and 0.8458(95% CI:0.775–0.917) for the task of depression versus comorbidity of anxiety and depression, respectively. The clinical utility was evaluated using Decision curve analysis (DCA) and calibration curves. The proposed model has potential clinical application prospects and may assist clinicians to improve the accuracy and efficiency of diagnosis, and thereby facilitating timely appropriate intervention measures for patients with psychiatric disorders. Not applicable.

Background: Heart diseases continue to be among the primary causes of morbidity and mortality worldwide, highlighting the need for novel diagnostic methods to accurately, early, and economically predict and diagnose heart diseases. Current physical explores upon expert knowledge, possibly missing intricate and non-linear associations between heart disease risk factors. This is where machine learning (ML)methods emerge as promising alternatives for prediction and diagnosis of heart diseases by emphasizing data availability. Methods: This paper attempts to find out how six popular machine learning algorithms namely, Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting (GB), K-Nearest Neighbors (KNN), and eXtreme Gradient Boosting (XGB) perform on a publicly available heart disease dataset collected from Kaggle. For better performance and to make them comparable to each other, basic preprocessing techniques have also been implemented. The performance of all these algorithms will be measured by their respective definitions of accuracy, precision, recall, and F1 score. Result: With an F1-score of 98.52%, Random Forest and XGBoost surpassed other models, yielding a superior accuracy in predictions, measuring 98.53%. The models recorded a perfect level of precision, 100%, coupled with high values of recall, exceeding 97%. Gradient Boosting recorded impressive results, receiving an accuracy score of 93.17%. Conclusion: The research findings revealed that the Random Forest and XGBoost models are very effective in the prediction and diagnosis of heart disease. These two models have the capacity to support a clinical decision. Novelty: That is to say, the novelty of this research is that it evaluates multiple machine learning models with comparison to each other. The role of machine learning in enhancing cardiovascular disease diagnosis as well as decision support systems in healthcare and educating and aware the people about heart care and health.

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.

This study aimed to utilize various machine learning algorithms to develop a predictive model for the progression of severe community-acquired pneumonia (SCAP) in children to critical severe community-acquired pneumonia (cSCAP). Retrospective analysis of clinical data of SCAP patients admitted to the Department of Pediatric Intensive Care Medicine at the First Affiliated Hospital of Bengbu Medical University from January 2021 to April 2023. Logistic regression (LR) and Least Absolute Shrinkage and Selection Operator (LASSO) were jointly employed to screen model variables. The selected variables were then incorporated into seven algorithms, namely LR, Decision Tree (DT), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Naive Bayes (NB), k-Nearest Neighbor (KNN), and Support Vector Machine (SVM), to establish a predictive model for the progression of SCAP in children to a critically severe stage. The effectiveness of the model was evaluated based on the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score. Finally, the Shapley Additive Explanation (SHAP) algorithm was used to interpret the established machine learning model. A total of 211 patients were included. Red Cell Distribution Width–Coefficient of Variation (RDW-CV), procalcitonin (PCT), blood urea nitrogen (BUN), and lactate dehydrogenase (LDH) were selected as predictors. The XGBoost model outperformed six other algorithms, with an AUC of 0.98 (95% CI,0.93–1.00 ), accuracy 0.89 (95% CI, 0.78–0.94), sensitivity 0.98 (95% CI, 0.95–1.00), and specificity 0.75 (95% CI, 0.45–0.87). SHAP analysis identified PCT, LDH, RDW-CV, and BUN as the most important contributors, supporting their clinical relevance for early risk stratification. This study developed an accurate predictive model for the cSCAP in children using machine learning techniques, providing clinical support for decision-making by clinicians.

The technology known as cemented paste backfill (CPB) has gained considerable popularity worldwide. Yield stress (YS) is a significant factor considered in the assessment of CPB’s flowability or transportability. The minimal shear stress necessary to start the flow is known as Yield stress (YS), and it serves as an excellent measure of the strength of the particle-particle interaction. The traditional evaluation and measurement of YS performed by experimental tests are time-consuming and costly, which induces delays in construction projects. Moreover, the YS of CPB depends on numerous factors such as cement/tailing ratio, solid content and oxide content of tailing. Therefore, in order to simplify YS estimation and evaluation, the Artificial Intelligence (AI) approaches including eight Machine Learning techniques such as the Extreme Gradient Boosting algorithm, Gradient Boosting algorithm, Random Forest algorithm, Decision Trees, K-Nearest Neighbor, Support Vector Machine, Multivariate Adaptive Regression Splines and Gaussian Process are used to build the soft-computing model in predicting the YS of CPB. The performance of these models is evaluated by three metrics coefficient of determination (R2), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The 3 best models were found to predict the Yield Stress of CPB (Gradient Boosting (GB), Extreme Gradient Boosting (XGB) and Random Forest (RF), respectively) with the 3 metrics of the three models, respectively, GB {R2 = 0.9811, RMSE = 0.1327 MPa, MAE = 0.0896 MPa}, XGB {R2 = 0.9034, RMSE = 0.3004 MPa, MAE = 0.1696 MPa} and RF {R2 = 0.8534, RMSE = 0.3700 MPa, MAE = 0.1786 MPa}, for the testing dataset. Based on the best performance model including GB, XG and RF, the other AI techniques such as SHapley Additive exPlanations (SHAP), Permutation Importance, and Individual Conditional Expectation (ICE) are also used for evaluating the factor effect on the YS of CPB. The results of this investigation can help the engineers to accelerate the mixed design of CPB.

Exploring sex classification from earprints - A comparison of supervised machine learning algorithms and conventional linear discriminant analysis.

Background: Accurate diagnosis of Parkinson’s disease (PD) is challenging due to its diverse manifestations. Machine learning (ML) algorithms can improve diagnostic precision, but their generalizability across medical centers in China is underexplored. Objective: To assess the accuracy of an ML algorithm for PD diagnosis, trained and tested on data from different medical centers in China. Methods: A total of 1656 participants were included, with 1028 from Beijing (training set) and 628 from Fuzhou (external validation set). Models were trained using the least absolute shrinkage and selection operator–logistic regression (LASSO-LR), decision tree (DT), random forest (RF), eXtreme gradient boosting (XGboost), support vector machine (SVM), and k-nearest neighbor (KNN) techniques. Hyperparameters were optimized using five-fold cross-validation and grid search techniques. Model performance was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve, accuracy, sensitivity (recall), specificity, precision, and F1 score. Variable importance was assessed for all models. Results: SVM demonstrated the best differentiation between healthy controls (HCs) and PD patients (AUC: 0.928, 95% CI: 0.908–0.947; accuracy: 0.844, 95% CI: 0.814–0.871; sensitivity: 0.826, 95% CI: 0.786–0.866; specificity: 0.861, 95% CI: 0.820–0.898; precision: 0.849, 95% CI: 0.807–0.891; F1 score: 0.837, 95% CI: 0.803–0.868) in the validation set. Constipation, olfactory decline, and daytime somnolence significantly influenced predictability. Conclusion: We identified multiple pivotal variables and SVM as a precise and clinician-friendly ML algorithm for prediction of PD in Chinese patients.