Exploring the dominant features and data-driven detection of polycystic ovary syndrome through modified stacking ensemble machine learning technique

Intrusion detection plays a critical role in safeguarding computer networks from malicious activities and unauthorized access. Traditional machine learning (ML) models have been widely employed for intrusion detection, but their effectiveness can be limited due to the complex and evolving nature of network attacks. An efficient Convolutional Neural Network (CNN) that utilizes one-dimensional convolutions, batch normalization, and max pooling layers is designed to capture the attack patterns in network traffic data. By leveraging the inherent feature extraction capabilities of CNNs, the proposed architecture demonstrates improved performance in identifying network intrusions compared to traditional ML models. The effectiveness of the novel CNN architecture is evaluated by conducted comprehensive experiments on two benchmark intrusion detection datasets. The designed CNN performance was compared with four other ML models such as logistic regression, k-nearest neighbors, decision trees, and random forest. The experimental results revealed that the proposed CNN architecture outperformed the traditional ML models with highest accuracy of 97.68% and 97.39% and highest F1-score of 97.68% and 97.40% for. UNSW-NB15 and CICIDS2017 datasets, respectively. The proposed CNN architecture serves as a promising alternative to traditional ML models, offering improved performance in identifying and mitigating network attacks.

ObjectivesTo develop and identify machine learning (ML) models using pretreatment 2-deoxy-2-[18F]fluoro-D-glucose ([18F]-FDG)-positron emission tomography (PET)-based radiomic features to differentiate benign from malignant parotid gland diseases (PGDs).Materials and methodsThis retrospective study included 62 patients with 63 PGDs who underwent pretreatment [18F]-FDG-PET/computed tomography (CT). The lesions were assigned to the training (n = 44) and testing (n = 19) cohorts. In total, 49 [18F]-FDG-PET-based radiomic features were utilized to differentiate benign from malignant PGDs using five different conventional ML algorithmic models (random forest, neural network, k-nearest neighbors, logistic regression, and support vector machine) and the deep learning (DL)-based ensemble ML model. In the training cohort, each conventional ML model was constructed using the five most important features selected by the recursive feature elimination method with the tenfold cross-validation and synthetic minority oversampling technique. The DL-based ensemble ML model was constructed using the five most important features of the bagging and multilayer stacking methods. The area under the receiver operating characteristic curves (AUCs) and accuracies were used to compare predictive performances.ResultsIn total, 24 benign and 39 malignant PGDs were identified. Metabolic tumor volume and four GLSZM features (GLSZM_ZSE, GLSZM_SZE, GLSZM_GLNU, and GLSZM_ZSNU) were the five most important radiomic features. All five features except GLSZM_SZE were significantly higher in malignant PGDs than in benign ones (each p < 0.05). The DL-based ensemble ML model had the best performing classifier in the training and testing cohorts (AUC = 1.000, accuracy = 1.000 vs AUC = 0.976, accuracy = 0.947).ConclusionsThe DL-based ensemble ML model using [18F]-FDG-PET-based radiomic features can be useful for differentiating benign from malignant PGDs.Second abstractThe DL-based ensemble ML model using [18F]-FDG-PET-based radiomic features can overcome the previously reported limitation of [18F]-FDG-PET/CT scan for differentiating benign from malignant PGDs. The DL-based ensemble ML approach using [18F]-FDG-PET-based radiomic features can provide useful information for managing PGD.

Harmful Algal Blooms (HABs), driven by environmental pollution, pose significant threats to water quality, public health, and aquatic ecosystems. This study aims to enhance the prediction of HABs in Lake Erie, part of the Great Lakes system, by utilizing ensemble machine learning (ML) models coupled with explainable artificial intelligence (XAI) for interpretability. Using water quality data from 2013 to 2020, various physical, chemical, and biological parameters were analyzed to predict chlorophyll-a (Chl-a) concentrations, a proxy for algal blooms. The study employed multiple ensemble ML models, including Random Forest (RF), Deep Forest (DF), Gradient Boosting (GB), and XGBoost, and compared their performance against individual models such as Support Vector Machine (SVM), Decision Tree (DT), and Multi-Layer Perceptron (MLP). The findings reveal that ensemble models, particularly XGBoost and Deep Forest (DF), achieve superior predictive accuracy with R² values of 0.8517 and 0.8544, respectively. The application of SHapley Additive exPlanations (SHAP) provided insights into the relative importance of input features, identifying Particulate Organic Nitrogen (PON), Particulate Organic Carbon (POC), and Total Phosphorus (TP) as critical factors influencing Chl-a concentrations. This research demonstrates the effectiveness of integrating ensemble ML models with XAI to improve HAB prediction accuracy and interpretability. The results support the development of proactive water quality management strategies and highlight the potential of advanced ML techniques in environmental monitoring.

While the rapid development of vaccines during the COVID-19 pandemic helped save billions of lives, a significant percentage of the population reported adverse reactions after vaccination. Post-vaccination surveys were conducted to understand the possible side effects. Analyzing and understanding the side effects enables the relevant stakeholders to gain more understanding of the vaccine and allows the individual to make informed decisions about whether to receive the vaccine. This work aims to identify a robust approach that can be used to predict the possibility of potential adverse symptoms. A dataset comprising 840 participants with 15 Post-Vaccination Symptoms was considered for the study. Synthetic Minority Oversampling TEchnique (SMOTE) was used to handle the class imbalance of the dataset. A combination of SMOTE and ensemble machine learning models was used to predict the adverse reactions to COVID-19 vaccines. The ensemble Machine Learning (ML) models that are considered for this study are Random Forest, Extreme Gradient Boosting Machine (XGBoost), Light Gradient Boosting Machine, and AdaBoost. The metrics accuracy, precision, recall and Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) were used to measure the performance of the models. The dataset was pre-processed to handle missing values and one-hot encoding was applied to convert categorical variables into the numerical format. Insights into the data distribution and relationships between variables were gained through exploratory data and correlation analysis, respectively. Class imbalance in the target variables was addressed using SMOTE, resulting in a significantly improved F1-score and ROC-AUC score. Among the ensemble ML models, XGBoost delivered the best performance metrics. A combined performance score was calculated by averaging the F1-score and accuracy to identify the best model. XGBoost obtained the highest performance score among the ensemble ML models, and its performance is further enhanced by performing the threshold adjustment using the maximum F1- score strategy. The findings suggest that the combination of SMOTE and ensemble learning models with threshold adjustment provides a more efficient prediction of adverse effects after COVID-19 vaccination, aiding in healthcare decision-making.

The increasing prevalence of malware presents a critical challenge to cybersecurity, emphasizing the need for robust detection methods. This study uses a binary tabular classification dataset to evaluate the impact of feature selection, feature scaling, and machine learning (ML) models on malware detection. The methodology involves experimenting with three feature scaling techniques (no scaling, normalization, and min-max scaling), three feature selection methods (no selection, Linear Discriminant Analysis (LDA), and Principal Component Analysis (PCA)), and twelve ML models, including traditional algorithms and ensemble methods. A publicly available dataset with 11,598 samples and 139 features is utilized, and model performance is assessed using metrics such as accuracy, precision, recall, F1-score, and AUC-ROC. Results reveal that the Light Gradient Boosting Machine (LGBM) achieves the highest accuracy of 97.16% when PCA and either min-max scaling or normalization are applied. Additionally, ensemble models consistently outperform traditional ML models, demonstrating their effectiveness in enhancing malware detection. These findings offer valuable insights into optimizing preprocessing and model selection strategies for developing reliable and efficient malware detection systems.

BackgroundDespite extensive research on keratoconus (KC) detection with traditional machine learning models, stacking ensemble learning approaches remain underexplored. This paper presents a stacking ensemble learning method to enhance automated KC screening.MethodsThis study utilizes a clinical dataset containing detailed corneal data from 2491 cases classified as non-KC (NKC), subclinical KC (SCKC) and clinical KC (CKC). Each cornea is represented by 79 features extracted from Pentacam imaging. Following extensive pre-processing, key corneal features that are strongly correlated with the target diagnosis are identified. These features are the keratometry of the steepest anterior point, surface variance index, vertical asymmetry index, height decentration index, and height asymmetry index. A novel stacking ensemble model is developed using the selected features to improve corneal classification into NKC, SCKC, and CKC by integrating top tree-based classifiers (random forest, gradient boosting, decision trees) with a support vector machine meta-classifier.ResultsThe pre-processing and feature selection techniques reduced the model's parameters to just 6.33% of the original dataset, improving classification performance, and cutting over 85% of the training time. The performance of the developed model was validated and tested on unseen data. Experimental results showed that the model outperforms existing studies, achieving 99.72% accuracy, precision, sensitivity, F1, and F2 scores, with a Matthews correlation coefficient of 0.995. It accurately classified all NKC and CKC cases, with just one misclassification involving an SCKC case. The model also demonstrated consistent performance on 100 additional unseen test cases, underscoring its generalizability and robustness in KC screening.ConclusionsBy combining the strengths of diverse base models and key Pentacam indices, the stacking ensemble approach ensures reliable, accurate KC screening, providing clinicians with an automated tool for early detection and better patient management.

Graph theory provides a robust framework for modelling complex relationships in medical data, enhancing classification accuracy through relational learning. Unlike traditional machine learning (ML) models that treat data points independently, graph-based approaches support structural dependencies to improve feature representation. This study explores the application of Graph Attention Networks (GAT), GraphSAGE, Graph Convolutional Network(GCN) and Graph Isomorphism Network (GIN) for breast cancer classification, employing k-nearest neighbor (KNN) graphs to construct a structured dataset where nodes represent patients and edges capture feature similarities. The effectiveness of the graph-based approaches is evaluated against traditional ML classifiers, including Decision Trees, Random Forest, LightGBM, and XGBoost. Experimental results indicate that GCN, GIN, GAT and GraphSAGE are beat conventional methods, with GCN, GAT and GraphSAGE achieved 100% test-accuracy and with GIN achieved 99.42%, to confirm the percentage of accuracy, authors conducted extensive experiments, including robustness testing by reducing KNN connections, introducing noise, and shuffling train-test splits. Results demonstrate that graph-based models are significantly best than traditional ML models, and these graph-based models maintain same classification accuracy while maintaining stability under robustness tests. The findings confirm that graph-based learning provides a scalable, interpretable, and highly accurate alternative for medical classification tasks, proving its effectiveness in distinguishing between benign and malignant tumors.

The identification of an effective inhibitor is an important starting step in drug development. Unfortunately, many issues such as the characterization of protein binding sites, the screening library, materials for assays, etc., make drug screening a difficult proposition. As the size of screening libraries increases, more resources will be inefficiently consumed. Thus, new strategies are needed to preprocess and focus a screening library towards a targeted protein. Herein, we report an ensemble machine learning (ML) model to generate a CDK8-focused screening library. The ensemble model consists of six different algorithms optimized for CDK8 inhibitor classification. The models were trained using a CDK8-specific fragment library along with molecules containing CDK8 activity. The optimized ensemble model processed a commercial library containing 1.6 million molecules. This resulted in a CDK8-focused screening library containing 1,672 molecules, a reduction of more than 99.90%. The CDK8-focused library was then subjected to molecular docking, and 25 candidate compounds were selected. Enzymatic assays confirmed six CDK8 inhibitors, with one compound producing an IC50 value of ≤100 nM. Analysis of the ensemble ML model reveals the role of the CDK8 fragment library during training. Structural analysis of molecules reveals the hit compounds to be structurally novel CDK8 inhibitors. Together, the results highlight a pipeline for curating a focused library for a specific protein target, such as CDK8.

Understanding the spatial distribution of soil organic carbon (SOC) content over different climatic regions will enhance our knowledge of carbon gains and losses due to climatic change. However, little is known about the SOC content in the contrasting arid and sub-humid regions of Iran, whose complex SOC–landscape relationships pose a challenge to spatial analysis. Machine learning (ML) models with a digital soil mapping framework can solve such complex relationships. Current research focusses on ensemble ML models to increase the accuracy of prediction. The usual ensemble method is boosting or weighted averaging. This study proposes a novel ensemble technique: the stacking of multiple ML models through a meta-learning model. In addition, we tested the ensemble through rescanning the covariate space to maximize the prediction accuracy. We first applied six state-of-the-art ML models (i.e., Cubist, random forests (RF), extreme gradient boosting (XGBoost), classical artificial neural network models (ANN), neural network ensemble based on model averaging (AvNNet), and deep learning neural networks (DNN)) to predict and map the spatial distribution of SOC content at six soil depth intervals for both regions. In addition, the stacking of multiple ML models through a meta-learning model with/without rescanning the covariate space were tested and applied to maximize the prediction accuracy. Out of six ML models, the DNN resulted in the best modeling accuracies, followed by RF, XGBoost, AvNNet, ANN, and Cubist. Importantly, the stacking of models indicated a significant improvement in the prediction of SOC content, especially when combined with rescanning the covariate space. For instance, the RMSE values for SOC content prediction of the upper 0–5 cm of the soil profiles of the arid site and the sub-humid site by the proposed stacking approaches were 17% and 9% respectively, less than that obtained by the DNN models—the best individual model. This indicates that rescanning the original covariate space by a meta-learning model can extract more information and improve the SOC content prediction accuracy. Overall, our results suggest that the stacking of diverse sets of models could be used to more accurately estimate the spatial distribution of SOC content in different climatic regions.

Physics-supervised ensemble learning model for predicting failure modes of reinforced concrete columns

A study on road accident prediction and contributing factors using explainable machine learning models: analysis and performance

Polycystic ovary syndrome is one of the most common types of endocrine and metabolic diseases in women of reproductive age that needs to be screened early and assessed non-invasively. The objective of the current study was to develop prediction models for polycystic ovary syndrome based on data of tongue and pulse using machine learning techniques. A dataset of 285 polycystic ovary syndrome patients and 201 healthy women were investigated to identify the significant tongue and pulse parameters for predicting polycystic ovary syndrome. In this study, feature selection was performed using least absolute shrinkage and selection operator regression. Several machine learning algorithms (multilayer perceptron classifier, eXtreme gradient boosting classifier, and support vector machine) were used to construct the classification models to predict the presence of polycystic ovary syndrome. TB-L, TB-a, TB-b, TC-L, TC-a, h3, and h4/h1 in tongue and pulse parameters were statistically associated with polycystic ovary syndrome presence. Among the several machine learning techniques, the support vector machine model was optimal for the comprehensive evaluation of this dataset and deduced the area under the receiver operating characteristic curve, DeLong test, calibration curve, and decision curve analysis. The machine learning model with tongue and pulse factors can predict the existence of polycystic ovary syndrome precisely.

A systematic review and future research agenda on detection of polycystic ovary syndrome (PCOS) with computer-aided techniques

Performance investigation of osmotically assisted reverse osmosis using explainable machine learning models: A comparative study

BackgroundSepsis-associated acute kidney injury (S-AKI) is a major contributor to mortality in intensive care units (ICU). Early prediction of mortality risk is crucial to enhance prognosis and optimize clinical decisions. This study aims to develop a 28-day mortality risk prediction model for S-AKI utilizing an explainable ensemble machine learning (ML) algorithm.MethodsThis study utilized data from the Medical Information Mart for Intensive Care IV (MIMIC-IV 2.0) database to gather information on patients with S-AKI. Univariate regression, correlation analysis and Boruta were combined for feature selection. To construct the four ML models, hyperparameters were tuned via random search and five-fold cross-validation. To evaluate the performance of all models, ROC, K-S, and LIFT curves were used. The discrimination of ML models and traditional scoring systems was compared using area under the receiver operating characteristic curve (AUC). Additionally, the SHapley Additive exPlanation (SHAP) was utilized to interpret the ML model and identify essential variables. To investigate the relationship between the top nine continuous variables and the risk of 28-day mortality. COX regression-restricted cubic splines were utilized while controlling for age and comorbidities.ResultsThe study analyzed data from 9,158 patients with S-AKI, dividing them into a 28-day mortality group of 1,940 and a survival group of 7,578. The results showed that XGBoost was the best performing model of the four ML models with AUC of 0.873. All models outperformed APS-III 0.713 and SAPS-II 0.681. The K-S and LIFT curves indicated XGBoost as the most effective predictor for 28-day mortality risk. The model’s performance was evaluated using ROCpr curves, calibration curves, accuracy, precision, and F1 scores. SHAP force plots were utilized to interpret and visualize the personalized predictive power of the 28-day mortality risk model. Additionally, COX regression restricted cubic splines revealed an interesting non-linear relationship between the top nine variables and 28-day mortality.ConclusionThe use of ensemble ML models has shown to be more effective than the LR model and conventional scoring systems in predicting 28-day mortality risk in S-AKI patients. By visualizing the XGBoost model with the best predictive performance, clinicians are able to identify high-risk patients early on and improve prognosis.