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 < 0.001), and ferritin (p < 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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Photograph-based machine learning approach for automated detection and differentiation of aerial blight disease in soybean crops by Mukta Nainwal, Anurag Satpathi, Saqib Shamsi, Ali Salem, Ajeet Singh Nain, Dinesh Kumar Vishwakarma, Ahmed Elbeltagi, Salah El-Hendawy, Mohamed A. Mattar

“…We evaluated nine machine learning algorithms, including logistic regression, Support Vector Machine (SVM), VGG-16 (with and without augmentation), ResNet-18 (with, without augmentation and larger image sizes) and ResNet-34 (with and without augmentation), for disease classification. …”
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A hybrid framework for heart disease prediction using classical and quantum-inspired machine learning techniques by Ankur Kumar, Sanjay Dhanka, Abhinav Sharma, Rohit Bansal, Mochammad Fahlevi, Fazla Rabby, Mohammed Aljuaid

“…A Support Vector Machine (SVM) classifier has been used in both classical and quantum domains. …”
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Unveiling the ageing-related genes in diagnosing osteoarthritis with metabolic syndrome by integrated bioinformatics analysis and machine learning by Jian Huang, Lu Wang, Jiangfei Zhou, Tianming Dai, Weicong Zhu, Tianrui Wang, Hongde Wang, Yingze Zhang

“…The limma package was used to identify differentially expressed genes (DEGs), and weighted gene coexpression network analysis (WGCNA) screened gene modules, and machine learning algorithms, such as random forest (RF), support vector machine (SVM), generalised linear model (GLM), and extreme gradient boosting (XGB), were employed. …”
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Enhancing predictive maintenance in automotive industry: addressing class imbalance using advanced machine learning techniques by Yashashree Mahale, Shrikrishna Kolhar, Anjali S. More

“…The on-board diagnostic dataset utilized has only 16.3% of the failure data, and to address this, 3 key approaches were explored: [i] synthetic minority oversampling technique (SMOTE), [ii] cost-sensitive learning, [iii] ensemble methods. Six machine learning models, including logistic regression, support vector machine, decision tree, and random forest, along with gradient boosting algorithms using extreme gradient boost (XGBoost) and light gradient boosting machine frameworks, were implemented. …”
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Machine Learning-Based Harvest Date Detection and Prediction Using SAR Data for the Vojvodina Region (Serbia) by Gordan Mimić, Amit Kumar Mishra, Miljana Marković, Branislav Živaljević, Dejan Pavlović, Oskar Marko

“…It is shown that the separability of unharvested and harvested data in two-dimensional space does not depend on the selected method but more on the crop itself. Support Vector Machine and Multi-layer Perceptron were used as classification algorithms for harvest detection, with the former achieving higher accuracies of 79.65% for wheat, 83.41% for maize and 95.97% for soybean. …”
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ML-AMPSIT: Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool by D. Di Santo, C. He, F. Chen, L. Giovannini

“…This tool leverages the strengths of multiple regression-based and probabilistic machine learning methods, including LASSO (see the list of abbreviations in Appendix B), support vector machine, classification and regression trees, random forest, extreme gradient boosting, Gaussian process regression, and Bayesian ridge regression. …”
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PROACTIVE MITIGATION OF DDoS IoT-RELATED ATTACK USING MACHINE LEARNING AND SOFTWARE DEFINED NETWORKING TECHNIQUES by Emmanuel J. Ebong, Samuel N. John, Dominic S. Nyitamen, Samuel F. Kolawole

“…The large dataset was scaled down using Min Max Scaler before the Machine Learning (ML) classification stage. Four (4) ML algorithms namely, Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT) and Random Forest (RF) were used to classify the models. …”
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Precision Soil Moisture Monitoring Through Drone-Based Hyperspectral Imaging and PCA-Driven Machine Learning by Milad Vahidi, Sanaz Shafian, William Hunter Frame

“…Our results demonstrate that PCA effectively detected critical variables for soil moisture estimation, with the ANN model outperforming other machine learning algorithms, including Random Forest (RF), Support Vector Regression (SVR), and Gradient Boosting (XGBoost). …”
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Machine Learning–Based Calibration and Performance Evaluation of Low-Cost Internet of Things Air Quality Sensors by Mehmet Taştan

“…To improve sensor accuracy, eight different machine learning (ML) algorithms were applied: Decision Tree (DT), Linear Regression (LR), Random Forest (RF), k-Nearest Neighbors (kNN), AdaBoost (AB), Gradient Boosting (GB), Support Vector Machines (SVM), and Stochastic Gradient Descent (SGD). …”
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Revolutionizing Nursing and Midwifery Informatics Curriculum Evaluation in Ghana: A Data-Driven Machine Learning Approach by Iven Aabaah, Japheth Kodua Wiredu, Bakaweri Emmanuel Batowise, Nelson Abuba Seidu

“…The study employed Random Forest, Gradient Boosting, Support Vector Machine, K-Nearest Neighbor, and Logistic Regression algorithms, evaluated using standard performance metrics, including accuracy, precision, and recall. …”
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Machine learning of whole-brain resting-state fMRI signatures for individualized grading of frontal gliomas by Yue Hu, Xin Cao, Hongyi Chen, Daoying Geng, Kun Lv

“…The logical regression, random forest, support vector machine (SVM) and adaptive boosting algorithms were used to establish models. …”
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Machine learning-driven prediction of medical expenses in triple-vessel PCI patients using feature selection by Kuan-Yu Chen, Yen-Chun Huang, Chih-Kuang Liu, Shao-Jung Li, Mingchih Chen

“…The machine learning algorithms used included linear regression (LR), random forest (RF), support vector regression (SVR), generalized linear model boost (GLMBoost), Bayesian generalized linear model (BayesGLM), and extreme gradient boosting (eXGB). …”
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Climate-Based Prediction of Rice Blast Disease Using Count Time Series and Machine Learning Approaches by Meena Arumugam Gopalakrishnan, Gopalakrishnan Chellappan, Santhosh Ganapati Patil, Santosha Rathod, Kamalakannan Ayyanar, Jagadeeswaran Ramasamy, Sathyamoorthy Nagaranai Karuppasamy, Manonmani Swaminathan

“…In this study, weather-based models were developed based on count time series and machine learning techniques like INGARCHX, Artificial Neural Networks (ANNs), and Support Vector Regression (SVR), to forecast the incidence of rice blast disease. …”
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Enhanced cardiovascular risk prediction in the Western Pacific: A machine learning approach tailored to the Malaysian population. by Sazzli Kasim, Putri Nur Fatin Amir Rudin, Sorayya Malek, Nurulain Ibrahim, Xue Ning Kiew, Nafiza Mat Nasir, Khairul Shafiq Ibrahim, Raja Ezman Raja Shariff

“…<h4>Methods</h4>Utilizing data from the REDISCOVER Registry (5,688 participants from 2007 to 2017), 30 clinically relevant features were selected, and several ML algorithms were trained: Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Neural Network (NN) and Naive Bayes (NB). …”
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A Genetic Algorithm and PCA-Based Feature Selection to Improve the Failure Diagnosis Performance of Railway Vehicle Doors by Gil Hyun Kang, Ho Cheol Ki, Sang Hyun An, Juhee Choi, Chul Su Kim

“…Finally, the combination of the proposed methods was compared with individual methods to validate the classification performance by using support vector machine and other classifiers. It was confirmed that the proposed combination method shows the highest classification accuracy of 99.84&#x0025;.…”
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Classification of primary glomerulonephritis using machine learning models: a focus on IgA nephropathy prediction by Zhengbiao Hu, Shuangshan Bu, Kai Wang, Qianqian Cao, Huanhuan Zheng, Jie Yang, Shanshan Chen, Yuemeng Wu, Wenkai Ren, Chenlei He

“…The RF method was applied to select 17 key features for building diagnostic models, including the RF model, support vector machine (SVM), adaptive boosting (ADB), and traditional doctor judgment. …”
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Identification of clinical diagnostic and immune cell infiltration characteristics of acute myocardial infarction with machine learning approach by Huali Jiang, Weijie Chen, Benfa Chen, Tao Feng, Heng Li, Dan Li, Shanhua Wang, Weijie Li

“…Weighted Gene Co-expression Network Analysis (WGCNA) was used to identify significant module genes associated with AMI. Machine learning algorithms (Support Vector Machine (SVM), Random Forest (RF) and Least Absolute Shrinkage and Selection Operator (LASSO)) were applied to identify hub genes. …”
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Enhancing Typhoid Fever Diagnosis Based on Clinical Data Using a Lightweight Machine Learning Metamodel by Fariha Ahmed Nishat, M. F. Mridha, Istiak Mahmud, Meshal Alfarhood, Mejdl Safran, Dunren Che

“…<b>Methods:</b> A custom dataset comprising 14 clinical and demographic parameters—including age, gender, headache, muscle pain, nausea, diarrhea, cough, fever range (°F), hemoglobin (g/dL), platelet count, urine culture bacteria, calcium (mg/dL), and potassium (mg/dL)—was analyzed. A machine learning metamodel, integrating Support Vector Machine (SVM), Gaussian Naive Bayes (GNB), and Decision Tree classifiers with a Light Gradient Boosting Machine (LGBM), was trained and evaluated using k-fold cross-validation. …”
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Quality Assessment of MRI-Radiomics-Based Machine Learning Methods in Classification of Brain Tumors: Systematic Review by Shailesh S. Nayak, Saikiran Pendem, Girish R. Menon, Niranjana Sampathila, Prakashini Koteshwar

“…Various imaging modalities, including MRI, PET/CT, and advanced techniques like ASL and DTI, were utilized to extract radiomic features for analysis. Machine learning algorithms such as deep learning networks, support vector machines, random forests, and logistic regression were applied to develop predictive models. …”
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