Explainable Artificial Intelligence to Predict the Water Status of Cotton (<i>Gossypium hirsutum</i> L., 1763) from Sentinel-2 Images in the Mediterranean Area by Simone Pietro Garofalo, Anna Francesca Modugno, Gabriele De Carolis, Nicola Sanitate, Mesele Negash Tesemma, Giuseppe Scarascia-Mugnozza, Yitagesu Tekle Tegegne, Pasquale Campi

“…Different machine learning algorithms, including random forest, support vector regression, and extreme gradient boosting, were evaluated using Sentinel-2 spectral bands as predictors. …”
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Development and Validation of a Clinical Risk Model for Predicting Malignancy in Patients with Thyroid Nodules by Shiva Borzouei, Ali Safdari, Erfan Ayubi

“…The diagnostic performance of the GLM was compared with five machine learning (ML) algorithms, including linear discriminant analysis (LDA), random forest, neural network, support vector machine, and k-nearest neighbor.  …”
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Presenting a prediction model for HELLP syndrome through data mining by Boshra Farajollahi, Mohammadjavad Sayadi, Mostafa Langarizadeh, Ladan Ajori

“…Results A total of 21 variables were included in this study after the first stage. Among all the ML algorithms, multi-layer perceptron and deep learning performed the best, with an F1 score of more than 99%.In all three evaluation scenarios of 5fold and 10fold cross-validation, the K-nearest neighbors (KNN), random forest (RF), AdaBoost, XGBoost, and logistic regression (LR) had an F1 score of over 0.95, while this value was around 0.90 for support vector machine (SVM), and the lowest values were below 0.90 for decision tree (DT). …”
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Predicting Treatment Outcomes in Patients with Low Back Pain Using Gene Signature-Based Machine Learning Models by Youzhi Lian, Yinyu Shi, Haibin Shang, Hongsheng Zhan

“…These key genes were then used to train 45 machine learning models by combining nine different algorithms: Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Decision Tree, Random Forest, Gradient Boosting Machine, Multilayer Perceptron, Naive Bayes, and Linear Discriminant Analysis. …”
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Prediction of the volume of shallow landslides due to rainfall using data-driven models by J. Tuganishuri, C.-Y. Yune, G. Kim, S. W. Lee, M. D. Adhikari, S.-G. Yum

“…The objectives of this research are to construct a model using advanced data-driven algorithms (i.e., ordinary least squares or linear regression (OLS), random forest (RF), support vector machine (SVM), extreme gradient boosting (EGB), generalized linear model (GLM), decision tree (DT), deep neural network (DNN), <span class="inline-formula"><i>k</i></span>-nearest-neighbor (KNN), and ridge regression (RR) algorithms) for the prediction of the volume of landslides due to rainfall, considering geological, geomorphological, and environmental conditions. …”
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Explainable machine learning model and nomogram for predicting the efficacy of Traditional Chinese Medicine in treating Long COVID: a retrospective study by Jisheng Zhang, Yang Chen, Aijun Zhang, Yi Yang, Liqian Ma, Hangqi Meng, Jintao Wu, Kean Zhu, Jiangsong Zhang, Ke Lin, Xianming Lin

“…Data from 1,204 patients served as the training set, while 127 patients formed the testing set.ResultsWe employed five ML algorithms: Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), and Neural Network (NN). …”
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Forecasting readmission in COVID-19 patients utilizing blood biomarkers and machine learning in the Hospital-at-Home program by Maria Glòria Bonet-Papell, Maria Glòria Bonet-Papell, Georgina Company-Se, María Delgado-Capel, Beatriz Díez-Sánchez, Lourdes Mateu-Pruñosa, Roger Paredes-Deirós, Jordi Ara del Rey, Lexa Nescolarde

“…Various classification algorithms (bagged trees, KNN, LDA, logistic regression, Naïve Bayes, and the support vector machine [SVM]) were implemented to predict readmission, with performance evaluated using accuracy, sensitivity, specificity, F1 score, and the Matthews Correlation Coefficient (MCC).ResultsSignificant differences were observed in IL-6, Hs-TnT, CRP (p &lt; 0.001), and ferritin (p &lt; 0.01) between the first day of conventional hospitalization and the first day of HaH for patients who were not readmitted. …”
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Prediction of Work-relatedness of Shoulder Musculoskeletal Disorders as by Using Machine Learning by Saemi Jung, Bogeum Kim, Yoon-Ji Kim, Eun-Soo Lee, Dongmug Kang, Youngki Kim

“…Additionally, machine learning algorithms, including logistic regression, support vector machine, decision tree, random forest, and the XGBoost, were utilized to construct prediction models for work-relatedness assessment. …”
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A comprehensive machine learning for high throughput Tuberculosis sequence analysis, functional annotation, and visualization by Md. Saddam Hossain, Md. Parvez Khandocar, Farzana Akter Riti, Md. Yeakub Ali, Prithbey Raj Dey, S M Jahurul Haque, Amira Metouekel, Atrsaw Asrat Mengistie, Mohammed Bourhia, Farid Khallouki, Khalid S. Almaary

“…We trained ML-supervised algorithms like XG Boost, Logistic Regression, Random Forest Classifier, Ad- aBoost, and Support Vector Machine to help classify TB patients from large RNA-sequence count data. …”
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Automated Cough Analysis with Convolutional Recurrent Neural Network by Yiping Wang, Mustafaa Wahab, Tianqi Hong, Kyle Molinari, Gail M. Gauvreau, Ruth P. Cusack, Zhen Gao, Imran Satia, Qiyin Fang

“…A number of machine learning algorithms were studied and compared, including decision tree, support vector machine, k-nearest neighbors, logistic regression, random forest, and neural network. …”
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Machine Learning-Based Alfalfa Height Estimation Using Sentinel-2 Multispectral Imagery by Hazhir Bahrami, Karem Chokmani, Saeid Homayouni, Viacheslav I. Adamchuk, Rami Albasha, Md Saifuzzaman, Maxime Leduc

“…Three machine learning algorithms were employed to estimate plant height from satellite images: random forest (RF), support vector regression (SVR), and extreme gradient boosting (XGB). …”
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A Machine Learning–Based Prediction Model for Acute Kidney Injury in Patients With Community-Acquired Pneumonia: Multicenter Validation Study by Mengqing Ma, Caimei Chen, Dawei Chen, Hao Zhang, Xia Du, Qing Sun, Li Fan, Huiping Kong, Xueting Chen, Changchun Cao, Xin Wan

“…ObjectiveThis study aimed to establish and validate predictive models for AKI in hospitalized patients with CAP based on machine learning algorithms. MethodsWe trained and externally validated 5 machine learning algorithms, including logistic regression, support vector machine, random forest, extreme gradient boosting, and deep forest (DF). …”
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Development and validation of a radiomic prediction model for TACC3 expression and prognosis in non-small cell lung cancer using contrast-enhanced CT imaging by Weichao Bai, Xinhan Zhao, Qian Ning

“…The radiomics model was constructed using logistic regression (LR) and support vector machine (SVM) algorithms. …”
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Machine learning predictive performance in road accident severity: A case study from Thailand by Ittirit Mohamad, Sajjakaj JomnonKwao, Vatanavongs Ratanavaraha

“…Eight algorithms were assessed, including Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (kNN), Neural Network (NN), Naïve Bayes (NB), Logistic Regression (LR), and Gradient Boosting (GB).A dataset comprising 112,837 road accidents over a five-year period in Thailand was analyzed, focusing exclusively on incidents where drivers were at fault. …”
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Machine learning models for discriminating clinically significant from clinically insignificant prostate cancer using bi-parametric magnetic resonance imaging by Hakan Ayyıldız, Okan İnce, Esin Korkut, Merve Gülbiz Dağoğlu Kartal, Atadan Tunacı, Şükrü Mehmet Ertürk

“…Once the features were extracted, Pearson’s correlation coefficient and selection were performed using wrapper-based sequential algorithms. The models were then built using support vector machine (SVM) and logistic regression (LR) machine learning algorithms. …”
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A Nomogram for Predicting Recurrence in Stage I Non‐Small Cell Lung Cancer by Rongrong Bian, Feng Zhao, Bo Peng, Jin Zhang, Qixing Mao, Lin Wang, Qiang Chen

“…In the discovery phase, two algorithms, least absolute shrinkage and selector operation and support vector machine‐recursive feature elimination, were used to identify candidate genes. …”
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Interpretable machine learning model for early prediction of disseminated intravascular coagulation in critically ill children by Jintuo Zhou, Yongjin Xie, Ying Liu, Peiguang Niu, Huajiao Chen, Xiaoping Zeng, Jinhua Zhang

“…Six machine learning algorithms—logistic regression (LR), extreme gradient boosting (XGB), random forest (RF), support vector machine (SVM), decision tree (DT), and k-nearest neighbor (KNN)—were employed to construct predictive models for DIC in critically ill children. …”
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Machine-learning-based reconstruction of long-term global terrestrial water storage anomalies from observed, satellite and land-surface model data by N. Mandal, P. Das, K. Chanda, K. Chanda

“…The most effective machine learning (ML) algorithms among convolutional neural network (CNN), support vector regression (SVR), extra trees regressor (ETR) and stacking ensemble regression (SER) models are evaluated at each grid cell to achieve optimal reproducibility. …”
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Prediction of Rice Chlorophyll Index (CHI) Using Nighttime Multi-Source Spectral Data by Cong Liu, Lin Wang, Xuetong Fu, Junzhe Zhang, Ran Wang, Xiaofeng Wang, Nan Chai, Longfeng Guan, Qingshan Chen, Zhongchen Zhang

“…Subsequently, CHI prediction models were developed using four machine learning algorithms: support vector regression (SVR), random forest (RF), back-propagation neural network (BPNN), and k-nearest neighbors (KNNs). …”
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Cerebrospinal Fluid Leakage Combined with Blood Biomarkers Predicts Poor Wound Healing After Posterior Lumbar Spinal Fusion: A Machine Learning Analysis by Pang Z, Ou Y, Liang J, Huang S, Chen J, Huang S, Wei Q, Liu Y, Qin H, Chen Y

“…In the test group, logistic regression analysis, support vector machine (SVM), random forest (RF), decision tree (DT), XGboost, Naïve Bayes (NB), k-Nearest Neighbor (KNN), and Multi-Layer Perceptron (MLP) were used to identify specific variables. …”
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