Enhancing clinical decision-making in closed pelvic fractures with machine learning models by Dian Wang, Yongxin Li, Li Wang

“…A total of 40 clinical variables were collected, and multiple machine learning algorithms were employed to develop predictive models, including logistic regression (LR), C5.0 Decision Tree (DT), Naive Bayes (NB), support vector machine (SVM), K-nearest neighbors (KNN), random Forest (RF), and artificial neural network (ANN). …”
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The Classification of Metastatic Spine Cancer and Spinal Compression Fractures by Using CNN and SVM Techniques by Woosik Jeong, Chang-Heon Baek, Dong-Yeong Lee, Sang-Youn Song, Jae-Boem Na, Mohamad Soleh Hidayat, Geonwoo Kim, Dong-Hee Kim

“…MRI data from Gyeongsang National University Hospital, collected from January 2019 to April 2022, were processed using Otsu’s binarization and Canny edge detection algorithms. Using these preprocessed datasets, convolutional neural network (CNN) and support vector machine (SVM) models were built. …”
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Construction of a machine learning-based prediction model for mitral annular calcification by LI Runqian, TAN Yanyi, GE Tiantian, QI Lei, BAI Song, TONG Jiayi

“…The subjects were randomly divided into a training set (350 cases) and a test set (150 cases) at a 7∶3 ratio. Nine machine learning algorithms, including logistic regression, relaxed support vector machines (RSVM) , decision tree, elastic net, multilayer perceptron, K-nearest neighbors, random forest, extreme gradient boosting (XGBoost) , and light gradient boosting machine (LightGBM) , were used to build prediction models for MAC. …”
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Identification of signature genes and subtypes for heart failure diagnosis based on machine learning by Yanlong Zhang, Yanming Fan, Fei Cheng, Dan Chen, Hualong Zhang

“…Furthermore, based on random forest, least absolute shrinkage and selection operator, and support vector machine algorithms, we finally identified four hub genes (FCN3, FREM1, MNS1, and SMOC2) that had good potential for diagnosis in HF (area under the curve > 0.7). …”
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Predicting postoperative trauma-induced coagulopathy in patients with severe injuries by machine learning by Xiaohui Du, Wei Wang, Bo Xu, Jiang Zheng, Victor W. Xia, Yi Guo, Shuai Feng, Qingxiang Mao, Hong Fu

“…The study employed various machine learning algorithms, including random forests, logistic regression, gradient boosting decision trees, support vector machines, backpropagation artificial neural networks, extreme gradient boosting, and naïve Bayes. …”
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Drought Detection in Satellite Imagery: A Layered Ensemble Machine Learning Approach by Muhammad Owais Raza, Naeem Ahmed Mahoto, Mana Saleh Al Reshan, Ali Alqazzaz, Adel Rajab, Asadullah Shaikh

“…The proposed approach combines conventional machine learning algorithms (Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), and k-Nearest Neighbor (k-NN)) with ensemble methods (Bagging and Voting) in a layered fashion for detecting drought from satellite imagery. …”
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Predicting postoperative neurological outcomes of degenerative cervical myelopathy based on machine learning by Shuai Zhou, Shuai Zhou, Shuai Zhou, Shuai Zhou, Zexiang Liu, Zexiang Liu, Zexiang Liu, Haoge Huang, Haoge Huang, Haoge Huang, Hanxu Xi, Xiao Fan, Xiao Fan, Xiao Fan, Yanbin Zhao, Yanbin Zhao, Yanbin Zhao, Xin Chen, Xin Chen, Xin Chen, Yinze Diao, Yinze Diao, Yinze Diao, Yu Sun, Yu Sun, Yu Sun, Hong Ji, Feifei Zhou, Feifei Zhou, Feifei Zhou

“…Five machine learning methods, namely, linear regression (LR), support vector machines (SVM), random forest (RF), XGBoost, and Light Gradient Boosting Machine (LightGBM), were used to predict whether patients achieved the minimal clinically important difference (MCID) in the improvement in the Japanese Orthopedic Association (JOA) score, which was based on basic information, symptoms, physical examination signs, intramedullary high signals on T2-weighted (T2WI) magnetic resonance imaging (MRI), and various scale scores. …”
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Implications of machine learning techniques for prediction of motor health disorders in Saudi Arabia by Ehab M. Almetwally, I. Elbatal, Mohammed Elgarhy, Amr R. Kamel

“…To detect motor disability cases based on several accuracy criteria, this study identified and assessed the performance of six major ML algorithms: decision trees (DT), naïve Bayes (NB), k-nearest neighbors (K-NN), support vector machines (SVM), artificial neural networks (ANNs), and random forest (RF). …”
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An integrated machine learning and fractional calculus approach to predicting diabetes risk in women by David Amilo, Khadijeh Sadri, Evren Hincal, Muhammad Farman, Kottakkaran Sooppy Nisar, Mohamed Hafez

“…We employ seven machine learning algorithms: Decision Tree, Logistic Regression, Support Vector Machine (SVM), Random Forest, Bagged Trees, Naive Bayes, and XGBoost, to identify key risk factors, with XGBoost demonstrating higher performance. …”
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Machine Learning Models in the Detection of MB2 Canal Orifice in CBCT Images by Shishir Shetty, Meliz Yuvali, Ilker Ozsahin, Saad Al-Bayatti, Sangeetha Narasimhan, Mohammed Alsaegh, Hiba Al-Daghestani, Raghavendra Shetty, Renita Castelino, Leena R David, Dilber Uzun Ozsahin

“…Six different ML models (logistic regression [LR], naïve Bayes [NB], support vector machine [SVM], K-nearest neighbours [Knn], random forest [RF], neural network [NN]) were then tested on their ability to classify the images into M and N. …”
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Identification of biomarkers for knee osteoarthritis through clinical data and machine learning models by Wei Chen, Haotian Zheng, Binglin Ye, Tiefeng Guo, Yude Xu, Zhibin Fu, Xing Ji, Xiping Chai, Shenghua Li, Qiang Deng

“…Based on these rankings, predictive models were constructed using Logistic Regression (LR), Random Forest (RF), eXtreme Gradient Boosting (xGBoost), Naive Bayes (NB), Support Vector Machine (SVM), and Decision Tree (DT) algorithms. …”
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Detection of Coffee Leaf Miner Using RGB Aerial Imagery and Machine Learning by Emerson Ferreira Vilela, Cileimar Aparecida da Silva, Jéssica Mayara Coffler Botti, Elem Fialho Martins, Charles Cardoso Santana, Diego Bedin Marin, Agnaldo Roberto de Jesus Freitas, Carolina Jaramillo-Giraldo, Iza Paula de Carvalho Lopes, Lucas de Paula Corrêdo, Daniel Marçal de Queiroz, Giuseppe Rossi, Gianluca Bambi, Leonardo Conti, Madelaine Venzon

“…The dataset was divided into training and testing subsets. A set of four machine learning algorithms was utilized: Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), and Stochastic Gradient Descent (SGD). …”
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An Ensemble Learning-Based Predictive Parameterization Approach for Permanent Magnet Synchronous Machines by Sema Nur Ipek, Nur Bekiroglu, Murat Taskiran

“…The study introduces an ensemble-based methodology for estimating the equivalent circuit parameters of PMSMs consisting of phase resistance (R), magnetizing reactance (<inline-formula> <tex-math notation="LaTeX">$X_{\mathrm {m}}$ </tex-math></inline-formula>), and leakage reactance (<inline-formula> <tex-math notation="LaTeX">$X_{\mathrm {l}}$ </tex-math></inline-formula>) via manufacturer catalog data, which eliminates the necessity for experimental setups, high-quality real-time data, and operational disruptions. Six machine learning models-Multilayer Perceptron (MLP), Cascade Forward Neural Network (CFNN), Layer Recurrent Neural Network (LRNN), Transformer-like Network (TRF), Decision Tree (DT), and Support Vector Regression (SVR)&#x2013;were evaluated in the first stage of the study. …”
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Predicting suicidality in people living with HIV in Uganda: a machine learning approach by Anthony B. Mutema, Anthony B. Mutema, Anthony B. Mutema, Lillian Linda, Lillian Linda, Daudi Jjingo, Segun Fatumo, Segun Fatumo, Eugene Kinyanda, Allan Kalungi, Allan Kalungi, Allan Kalungi

“…The model’s performance was evaluated using the area under the receiver operating characteristic curve (AUC), positive predictive value (PPV), sensitivity, specificity, and Mathew’s correlation coefficient (MCC).ResultsWe trained and evaluated eight different ML algorithms, including logistic regression, support vector machines, Naïve Bayes, k-nearest neighbors, decision trees, random forests, AdaBoost, and gradient-boosting classifiers. …”
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Comprehensive Outlier Detection in Wireless Sensor Network with Fast Optimization Algorithm of Classification Model by Haiqing Yao, Heng Cao, Jin Li

“…Recently, with high classification precision and affordable complexity, one-class quarter-sphere support vector machine (QSSVM) has been introduced to deal with the online and adaptive outlier detection in WSN. …”
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Acoustic-based machine learning approaches for depression detection in Chinese university students by Yange Wei, Yange Wei, Shisen Qin, Fengyi Liu, Rongxun Liu, Yunze Zhou, Yuanle Chen, Xingliang Xiong, Wei Zheng, Guangjun Ji, Yong Meng, Fei Wang, Fei Wang, Ruiling Zhang

“…Pearson correlation analyses were conducted to evaluate the relationship between acoustic features and Patient Health Questionnaire-9 (PHQ-9) scores. Five machine learning algorithms including Linear Discriminant Analysis (LDA), Logistic Regression, Support Vector Classification, Naive Bayes, and Random Forest were used to perform the classification. …”
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Stacking modeling with genetic algorithm-based hyperparameter tuning for uniaxial compressive strength prediction by Tanveer Alam Munshi, Khanum Popi, Labiba Nusrat Jahan, M. Farhad Howladar, Mahamudul Hashan

“…To forecast UCS, a number of methods were used, such as multilayer perceptron (MLP), random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost), and multiple variable regression (MVR). …”
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Identification of biomarkers and immune microenvironment associated with pterygium through bioinformatics and machine learning by Li-Wei Zhang, Ji Yang, Hua-Wei Jiang, Hua-Wei Jiang, Xiu-Qiang Yang, Ya-Nan Chen, Wei-Dang Ying, Ying-Liang Deng, Min-hui Zhang, Hai Liu, Hong-Lei Zhang

“…Additionally, we utilized weighted correlation network analysis (WGCNA) to select module genes and applied Random Forest (RF) and Support Vector Machine (SVM) algorithms to identify pivotal feature genes influencing pterygium progression. …”
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Sentiment Analysis of X Users Toward Electric Motorcycles Using SVM and BERT Algorithms by Calvin Adiwinata, Afiyati Afiyati

“…This study presents a comparative analysis of Support Vector Machine (SVM) and Bidirectional Encoder Representations from Transformers (BERT) for sentiment analysis on electric motorcycles in Indonesia using data from the social media platform X, formerly known as Twitter. …”
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Enhancing tool condition monitoring in friction stir welding with probabilistic neural network algorithm by Balachandar Krishnamurthy, Jegadeeshwaran Rakkiyannan

“…A feature importance study is conducted using a decision tree algorithm, which selects only the most significant features to reduce computational complexity.ResultFeature classification is then performed using various machine learning and deep learning algorithms, including Support Vector Machines (SVM), Multi-Layer Perceptron (MLP), Cascade Correlation, GMDH Polynomial Neural Networks, and Linear Discriminant Analysis Among these classifiers, Probabilistic Neural Networks (PNN) consistently deliver the best results as 91.25% under 1,400 rpm.DiscussionBased on these findings, the Probabilistic Neural Network algorithm is identified as a robust and reliable prediction model for monitoring FSW tool conditions.…”
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