Computer Aided Diagnostic System for Blood Cells in Smear Images Using Texture Features and Supervised Machine Learning by Shakhawan Hares Wady

“…The framework combines the features extracted by Center Symmetric Local Binary Pattern (CSLBP), Gabor Wavelet Transform (GWT), and Local Gradient Increasing Pattern (LGIP), the data was then fed into machine learning classifiers including Decision Tree (DT), Ensemble, K-Nearest Neighbor (KNN), Naïve Bayes (NB), and Random Forest (RF)).  …”
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IoT Network Anomaly Detection in Smart Homes Using Machine Learning by Nadeem Sarwar, Imran Sarwar Bajwa, Muhammad Zunnurain Hussain, Muhammad Ibrahim, Khizra Saleem

“…Attack categories include binary class, multiclass class, and subclasses. Results show Random Forest algorithm outperforms enough to use this methodology in smart environments.…”
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Anomaly-Based Intrusion Detection System in Wireless Sensor Networks Using Machine Learning Algorithms by Belal Al-Fuhaidi, Zainab Farae, Farouk Al-Fahaidy, Gawed Nagi, Abdullatif Ghallab, Abdu Alameri

“…It used different machine learning (ML) algorithms, random forest (RF), decision tree (DT), support vector machine (SVM), and K-nearest neighbors (KNNs) to analyze network traffic and binary classification or multiclass classification. …”
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Machine learning predictive performance in road accident severity: A case study from Thailand by Ittirit Mohamad, Sajjakaj JomnonKwao, Vatanavongs Ratanavaraha

“…Among the models tested, Random Forest demonstrated superior performance in the binary classification task, achieving an average class AUC of 0.768, classification accuracy of 0.777, precision of 0.752, and recall of 0.777. …”
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Machine learning and multicriteria analysis for prediction of compressive strength and sustainability of cementitious materials by Khuram Rashid, Fatima Rafique, Zunaira Naseem, Fahad K. Alqahtani, Idrees Zafar, Minkwan Ju

“…In the initial phase, three machine learning models—Decision Tree, Random Forest, and Multi-layer Perceptron—were developed and trained on a dataset of 1030 records to predict sustainable concrete's compressive strength accurately. …”
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Determination of 5-fluorouracil anticancer drug solubility in supercritical CO 2 using semi-empirical and machine learning models by Gholamhossein Sodeifian, Ratna Surya Alwi, Reza Derakhsheshpour, Nedasadat Saadati Ardestani

“…Three models with different approaches were applied to correlate and model the experimental data set: (i) seven density-based models, (ii) PR equations of state (vdW2 mixing rule), and (iii) machine learning-based models, namely non-linear regressions, Random Forest, Gradient Boosting, Decision Tree, and Kernel Ridge. …”
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A Unified Deep Learning Ensemble Framework for Voice-Based Parkinson’s Disease Detection and Motor Severity Prediction by Madjda Khedimi, Tao Zhang, Chaima Dehmani, Xin Zhao, Yanzhang Geng

“…To enhance prediction performance, ensemble learning strategies were applied by stacking outputs from the fusion model with tree-based regressors (Random Forest, Gradient Boosting, and XGBoost), using diverse meta-learners including XGBoost, Ridge Regression, and a deep neural network. …”
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Effectiveness of machine learning models in diagnosis of heart disease: a comparative study by Waleed Alsabhan, Abdullah Alfadhly

“…Our study employs a wide range of ML algorithms, such as Logistic Regression (LR), Naive Bayes (NB), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), K-Nearest Neibors (KNN), AdaBoost (AB), Gradient Boosting Machine (GBM), Light Gradient Boosting Machine (LGBM), CatBoost (CB), Linear Discriminant Analysis (LDA), and Artificial Neural Network (ANN) to assess the predictive performance of these algorithms in the context of heart disease detection. …”
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Hybrid Machine Learning Model for Hurricane Power Outage Estimation from Satellite Night Light Data by Laiyin Zhu, Steven M. Quiring

“…In general, the classification and regression tree-based machine learning models (XGBoost and random forest) demonstrated better performance than the logistic and CNN models in both binary classification and regression models. …”
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An Interpretable Machine Learning Framework for Athlete Motor Profiling Using Multi-Domain Field Assessments: A Proof-of-Concept Study by Bartosz Wilczyński, Maciej Biały, Katarzyna Zorena

“…Expert rules based on FMS quartiles and ≤−0.5 SD Z-scores for strength or balance generated the reference labels. The random forest model achieved 81% cross-validated accuracy (with balanced performance across classes) and 89% accuracy on the external handball group, exceeding the performance of the decision tree model. …”
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Utilizing SMOTE-TomekLink and machine learning to construct a predictive model for elderly medical and daily care services demand by Guangmei Yang, Guangdong Wang, Leping Wan, Xinle Wang, Yan He

“…To improve computational efficiency, we used three algorithms to develop prediction models, including Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Light Gradient Boosting Machine (LightGBM) algorithms. …”
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Data augmentation via diffusion model to enhance AI fairness by Christina Hastings Blow, Lijun Qian, Camille Gibson, Pamela Obiomon, Xishuang Dong

“…Five traditional machine learning models—Decision Tree (DT), Gaussian Naive Bayes (GNB), K-Nearest Neighbors (KNN), Logistic Regression (LR), and Random Forest (RF)—were used to validate the proposed approach.Results and discussionExperimental results demonstrate that the synthetic data generated by Tab-DDPM improves fairness in binary classification.…”
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Trade-offs between machine learning and deep learning for mental illness detection on social media by Zhanyi Ding, Zhongyan Wang, Yeyubei Zhang, Yuchen Cao, Yunchong Liu, Xiaorui Shen, Yexin Tian, Jianglai Dai

“…This study evaluates multiple ML models, including logistic regression, random forest, and LightGBM, alongside DL architectures such as ALBERT and Gated Recurrent Units (GRUs), for both binary and multi-class classification of mental health conditions. …”
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Explainable Machine Learning for Efficient Diabetes Prediction Using Hyperparameter Tuning, SHAP Analysis, Partial Dependency, and LIME by Md. Manowarul Islam, Habibur Rahman Rifat, Md. Shamim Bin Shahid, Arnisha Akhter, Md Ashraf Uddin, Khandaker Mohammad Mohi Uddin

“…The extra trees classifier (ET) performed exceptionally, achieving 97.23% accuracy on the multi‐class dataset and 97.45% on the binary dataset.…”
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Association between functional disability and depressive symptoms among older adults in rural China: a cross-sectional study by Hong Ding, Jian Rong, Xueqin Wang, Yanhong Ge, Guimei Chen

“…Data were analysed using SPSS statistics V.25.0 program with χ2 test, Mann-Whitney U test, binary logistic regression analysis and classification and regression tree (CART) model.Results The prevalence of depressive symptoms in 3336 interviewed older people was 52.94%. …”
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Ensemble-based model to investigate factors influencing road crash fatality for imbalanced data by Nazmus Sakib, Tonmoy Paul, Nafis Anwari, Md. Hadiuzzaman

“…It is the first to train eight distinct binary classification models: Classification and Regression Trees (CART), Random Forest (RF), Gradient Boosting Machine (GBM), Extreme Gradient Boost (XGBoost), Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Naïve Bayes (NB) under three strategies: in isolation, with bagging, and with optimized bagging techniques (Grid Search CV, Random Search CV, and Bayesian Optimization). …”
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Comparison of a machine learning model with a conventional rule-based selective dry cow therapy algorithm for detection of intramammary infections by S.M. Rowe, E. Zhang, S.M. Godden, A.K. Vasquez, D.V. Nydam

“…Machine learning (ML) algorithms evaluated were logistic regression, decision tree, random forest, light gradient-boosting machine, naive Bayes, and neural networks. …”
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Automatic diagnosis of extraocular muscle palsy based on machine learning and diplopia images by Xiao-Lu Jin, Xue-Mei Li, Tie-Juan Liu, Ling-Yun Zhou

“…A total of 2757 diplopia images were randomly selected as training data, while the test dataset contained 487 diplopia images. …”
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Post-Anesthesia Care Unit (PACU) readiness predictions using machine learning: a comparative study of algorithms by Shahnam Sedigh Maroufi, Maryam Soleimani Movahed, Azar Ejmalian, Maryam Sarkhosh, Ali Behmanesh

“…Two prediction approaches were tested: discharge timing in 15-minute intervals and binary classification (within 15 min or later). Models included Random Forest (RF), Support Vector Machines (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), Artificial Neural Network (ANN), and XGBoost, assessed via accuracy, precision, recall, F1 score, and AUC. …”
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Anemia Classification System Using Machine Learning by Jorge Gómez Gómez, Camilo Parra Urueta, Daniel Salas Álvarez, Velssy Hernández Riaño, Gustavo Ramirez-Gonzalez

“…We built a supervised learning approach and trained three models (Linear Discriminant Analysis, Decision Trees, and Random Forest) using an anemia dataset from a previous study by Sabatini in 2022. …”
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