Unsupervised detection of high-frequency oscillations in intracranial electroencephalogram: promoting a valuable automated diagnostic tool for epilepsy by Wenjing Chen, Tongzhou Kang, Md Belal Bin Heyat, Jamal E. Fatima, Yuanning Xu, Dakun Lai

“…Candidate HFOs are identified using STE and transformed into time-frequency maps using the continuous wavelet transform (CWT). The CVAE model is trained for dimensionality reduction and feature reconstruction, followed by clustering of the reconstructed maps using the K-means algorithm for automated HFOs detection.ResultsEvaluation of the proposed unsupervised method on clinical iEEG data demonstrates its superior performance compared to traditional supervised models. …”
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Prognostic prediction of gastric cancer based on H&E findings and machine learning pathomics by Guoda Han, Xu Liu, Tian Gao, Lei Zhang, Xiaoling Zhang, Xiaonan Wei, Yecheng Lin, Bohong Yin

“…Features selected via minimum Redundancy - Maximum Relevance (mRMR)- recursive feature elimination (RFE) screening were used to train a model using the Gradient Boosting Machine (GBM) algorithm. …”
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Developing the new diagnostic model by integrating bioinformatics and machine learning for osteoarthritis by Jian Du, Tian Zhou, Wei Zhang, Wei Peng

“…Finally, immune cell infiltration analysis was performed using CIBERSORT algorithm to explore the correlation between feature genes and immune cells. …”
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Identification of potential metabolic biomarkers and immune cell infiltration for metabolic associated steatohepatitis by bioinformatics analysis and machine learning by Haoran Xie, Junjun Wang, Qiuyan Zhao

“…Results: We successfully identified seven signature MRDEGs, including CYP7A1, GCK, AKR1B10, HPRT1, GPD1, FADS2, and ENO3, through PPI network analysis and machine learning algorithms. The gene model displayed exceptional diagnostic performance in the training and validation cohorts, as evidenced by the area under ROC curve (AUC) exceeding 0.9. …”
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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

“…Objective To develop a risk prediction model for mitral annular calcification (MAC) using various machine learning algorithms to enable early identification and risk assessment of MAC. …”
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Hyperspectral Imaging for Non-Destructive Moisture Prediction in Oat Seeds by Peng Zhang, Jiangping Liu

“…To further refine the predictive model, three feature selection methods—successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), and principal component analysis (PCA)—were assessed. …”
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MRI-based machine learning radiomics for prediction of HER2 expression status in breast invasive ductal carcinoma by Hong-Jian Luo, Jia-Liang Ren, Li mei Guo, Jin liang Niu, Xiao-Li Song

“…The performance of the models was evaluated using the area under the curve (AUC) of the operating characteristics (ROC). …”
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Inflammation-Driven Prognosis in Advanced Heart Failure: A Machine Learning-Based Risk Prediction Model for One-Year Mortality by Zhou M, Du X

“…Data were split into training and validation sets. Seven ML algorithms were applied to build and evaluate models. …”
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Advancing patient care: Machine learning models for predicting grade 3+ toxicities in gynecologic cancer patients treated with HDR brachytherapy. by Andres Portocarrero-Bonifaz, Salman Syed, Maxwell Kassel, Grant W McKenzie, Vishwa M Shah, Bryce M Forry, Jeremy T Gaskins, Keith T Sowards, Thulasi Babitha Avula, Adrianna Masters, Jose G Schneider, Scott R Silva

“…Seven supervised classification machine learning models (Logistic Regression, Random Forest, K-Nearest Neighbors, Support Vector Machines, Gaussian Naive Bayes, Multi-Layer Perceptron Neural Networks, and XGBoost) were constructed and evaluated. The training process involved sequential feature selection (SFS) when appropriate, followed by hyperparameter tuning. …”
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DiffuseGaitNet: Improving Parkinson’s Disease Gait Severity Assessment With a Diffusion Model Framework by Arshak Rezvani, Nasrin Ravansalar, Mohammad Ali Akhaee, Andrew J. Greenshaw, Russell Greiner, Maryam S. Mirian, Muhammad Yousefnezhad, Martin J. McKeown

“…In addition, we propose a novel classification algorithm that can learn a predictive model, from both observed training data and synthetic samples, to accurately assess PD severity. …”
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An Optimized Cascaded CNN Approach for Feature Extraction From Brain MRIs for Tumor Classification by Santosh Kumar Chhotray, Debahuti Mishra, Sarada Prasanna Pati, Sashikala Mishra

“…This study enhances brain tumor classification by leveraging pre-trained models and attention mechanisms, ultimately improving accuracy and reliability in medical imaging diagnostics through feature extraction. …”
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Deep learning-based automated segmentation and quantification of the dural sac cross-sectional area in lumbar spine MRI by George Ghobrial, Christian Roth

“…We implemented and assessed three deep learning models—U-Net, Attention U-Net, and MultiResUNet—using 5-fold cross-validation. The models were trained on T1-weighted axial MRI images and evaluated on metrics such as accuracy, precision, recall, F1-score, and mean absolute error (MAE).ResultsAll models exhibited a high correlation between predicted and actual DSCA values. …”
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Effects of different wearable sensors and locomotion tasks on machine learning-based joint moment prediction by Jonas Weber, Bernd J. Stetter

“…However, comparing different studies investigating various sensors and locomotion tasks can be challenging due to variations in ML algorithms, model evaluation techniques, and reported performance metrics (Gurchiek et al., 2019). …”
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Improving National Forest Mapping in Romania Using Machine Learning and Sentinel-2 Multispectral Imagery by Mohamed Islam Keskes, Aya Hamed Mohamed, Stelian Alexandru Borz, Mihai Daniel Niţă

“…This study evaluates the performance of three machine learning algorithms—Random Forest (RF), Classification and Regression Trees (CART), and the Gradient Boosting Tree Algorithm (GBTA)—in predicting the forest attributes from Sentinel-2 satellite imagery. …”
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Development of a machine learning prediction model for loss to follow-up in HIV care using routine electronic medical records in a low-resource setting by Tamrat Endebu, Girma Taye, Wakgari Deressa

“…Six supervised ML classifiers—J48 decision tree, random forest, K-nearest neighbors, support vector machine, logistic regression, and naïve Bayes—were utilized for training via Weka 3.8.6 software. The performance of each algorithm was evaluated through a 10-fold cross-validation approach. …”
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Sweet Potato Yield Prediction Using Machine Learning Based on Multispectral Images Acquired from a Small Unmanned Aerial Vehicle by Kriti Singh, Yanbo Huang, Wyatt Young, Lorin Harvey, Mark Hall, Xin Zhang, Edgar Lobaton, Johnie Jenkins, Mark Shankle

“…The performance of the ML algorithms is evaluated using various popular model performance metrics like R², RMSE, and MAE. …”
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Learning-based early detection of post-hepatectomy liver failure using temporal perioperative data: a nationwide multicenter retrospective study in ChinaResearch in context by Kai Wang, Qian Yang, Kang Li, Shanhua Tang, Baoluhe Zhang, Xiangyun Liao, Shunda Du, Wenguang Fu, Zhiwei Li, Huanwei Chen, Haorong Xie, Pengxiang Huang, Jieyuan Li, Qiuting Wang, Haiqing Liu, Zhiwei Huang, Pheng Ann Heng, Xueshuai Wan, Chuanjiang Li, Weixin Si

“…This China cohort was divided into 681 cases for training the deep-learning model and 1151 cases for validation. …”
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A clinical benchmark of public self-supervised pathology foundation models by Gabriele Campanella, Shengjia Chen, Manbir Singh, Ruchika Verma, Silke Muehlstedt, Jennifer Zeng, Aryeh Stock, Matt Croken, Brandon Veremis, Abdulkadir Elmas, Ivan Shujski, Noora Neittaanmäki, Kuan-lin Huang, Ricky Kwan, Jane Houldsworth, Adam J. Schoenfeld, Chad Vanderbilt

“…With the increase in availability of public foundation models of different sizes, trained using different algorithms on different datasets, it becomes important to establish a benchmark to compare the performance of such models on a variety of clinically relevant tasks spanning multiple organs and diseases. …”
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Performance Analysis of Real-Time Detection Transformer and You Only Look Once Models for Weed Detection in Maize Cultivation by Oscar Leonardo García-Navarrete, Jesús Hernán Camacho-Tamayo, Anibal Bregon Bregon, Jorge Martín-García, Luis Manuel Navas-Gracia

“…To reduce the influence of weeds, precision weeding is used, which uses image sensors and computational algorithms to identify plants and classify weeds using digital images. …”
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GrotUNet: a novel leaf segmentation method by Hongfei Deng, Hongfei Deng, Bin Wen, Bin Wen, Cheng Gu, Yingjie Fan

“…To address the above problems, this paper proposes GrotUNet, a novel leaf segmentation method that can be trained end-to-end. The algorithm is reconstructed in three aspects: semantic feature coding, hopping connectivity, and multiscale upsampling fusion. …”
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