A Novel Method for Mechanical Fault Diagnosis Based on Variational Mode Decomposition and Multikernel Support Vector Machine by Zhongliang Lv, Baoping Tang, Yi Zhou, Chuande Zhou

“…A novel fault diagnosis method based on variational mode decomposition (VMD) and multikernel support vector machine (MKSVM) optimized by Immune Genetic Algorithm (IGA) is proposed to accurately and adaptively diagnose mechanical faults. …”
Get full text

Predicting overactive bladder from inflammatory markers: A machine learning approach using NHANES 2005–2020 by Haoxun Zhang, Guoling Zhang, Chunyang Wang

“…This cross-sectional study analyzed data from 35,394 participants in the National Health and Nutrition Examination Survey (NHANES, 2005–2020) to evaluate associations between CBC-derived biomarkers—such as the Systemic Immune-Inflammation Index (SII), Systemic Inflammation Response Index (SIRI), and Neutrophil-to-Lymphocyte Ratio (NLR)—and OAB (defined by an OAB Symptom Score ≥3). Multivariable logistic regression, threshold analysis, and machine learning models (Random Forest [RF], Extreme Gradient Boosting) were employed, adjusting for sociodemographic, lifestyle, and clinical covariates. …”
Get full text

Exploring the link between the ZJU index and sarcopenia in adults aged 20–59 using NHANES and machine learning by Huan Chen, Ning Du, Hong Xiao, Zhao Wang

“…Subgroup analysis showed notable interactions with gender and diabetes (p < 0.05). Machine learning models consistently ranked ZJU, education level, and race as the most influential predictors of sarcopenia, emphasizing the interplay between metabolic health and socioeconomic factors. …”
Get full text

Toward Smart Condition Monitoring of Rotatory Machines: An Optimized Probabilistic Signal Reconstruction Methodology for Fault Prediction With Multisource Uncertainties by Xiaomo Jiang, Weijian Tang, Haixin Zhao, Xueyu Cheng

“…This is still a very challenging topic in various industrial fields because of data imperfection and multivariate correlation, as well as the variation in faults and components in different machines. …”
Get full text

Comparison of Machine-Learning Algorithms for Near-Surface Air-Temperature Estimation from FY-4A AGRI Data by Ke Zhou, Hailei Liu, Xiaobo Deng, Hao Wang, Shenglan Zhang

“…The spatial variation characteristics of the Tair error of the XGB method were less obvious than those of the other methods. The XGB model can provide more stable and high-precision Tair for a large-scale Tair estimation over China and can serve as a reference for Tair estimation based on machine-learning models.…”
Get full text

Personalized predictions of neoadjuvant chemotherapy response in breast cancer using machine learning and full-field digital mammography radiomics by Ye Ruan, Xingyuan Liu, Yantong Jin, Mingming Zhao, Xingda Zhang, Xiaoying Cheng, Yang Wang, Siwei Cao, Menglu Yan, Jianing Cai, Mengru Li, Bo Gao

“…The rad-score was calculated for each patient. Five machine learning classifiers were used to build radiomics models, and the optimal model was selected. …”
Get full text

Early detection of emerging SARS-CoV-2 Variants from wastewater through genome sequencing and machine learning by Xiaowei Zhuang, Van Vo, Michael A. Moshi, Ketan Dhede, Nabih Ghani, Shahraiz Akbar, Ching-Lan Chang, Angelia K. Young, Erin Buttery, William Bendik, Hong Zhang, Salman Afzal, Duane Moser, Dietmar Cordes, Cassius Lockett, Daniel Gerrity, Horng-Yuan Kan, Edwin C. Oh

Get full text

Machine-learning derived identification of prognostic signature to forecast head and neck squamous cell carcinoma prognosis and drug response by Sha-Zhou Li, Hai-Ying Sun, Yuan Tian, Liu-Qing Zhou, Tao Zhou

“…Therefore, the identification of reliable biomarker is crucial to enhance the accuracy of screening and treatment strategies for HNSCC.MethodTo develop and identify a machine learning-derived prognostic model (MLDPM) for HNSCC, ten machine learning algorithms, namely CoxBoost, elastic network (Enet), generalized boosted regression modeling (GBM), Lasso, Ridge, partial least squares regression for Cox (plsRcox), random survival forest (RSF), stepwise Cox, supervised principal components (SuperPC), and survival support vector machine (survival-SVM), along with 81 algorithm combinations were utilized. …”
Get full text

Machine Learning Method Application to Detect Predisposing Factors to Open-Pit Landslides: The Sijiaying Iron Mine Case Study by Jiang Li, Zhuoying Tan, Naigen Tan, Aboubakar Siddique, Jianshu Liu, Fenglin Wang, Wantao Li

Get full text

Machine learning identifies the association between second primary malignancies and postoperative radiotherapy in young-onset breast cancer patients. by Yulin Lai, Peiyuan Huang

“…As one of the main treatments for breast cancer YWBC patients, postoperative radiotherapy (PORT) may increase the risk of second primary malignancy (SPM).<h4>Methods</h4>Machine learning components, including ridge regression, XGBoost, k-nearest neighbor, light gradient boosting machine, logistic regression, support vector machine, neural network, and random forest, were used to construct a predictive model and identify the risk factors for SPMs with data from the Surveillance, Epidemiology and End Results. …”
Get full text

Method for EEG signal recognition based on multi-domain feature fusion and optimization of multi-kernel extreme learning machine by Shan Guan, Tingrui Dong, Long-kun Cong

“…Secondly, multivariate autoregressive (MVAR) model, wavelet packet decomposition, and Riemannian geometry methods are used to extract features from the time domain, frequency domain, and spatial domain, respectively, to construct a joint time-frequency-space feature vector. …”
Get full text

Establishment and Validation of a Machine‐Learning Prediction Nomogram Based on Lymphocyte Subtyping for Intra‐Abdominal Candidiasis in Septic Patients by Jiahui Zhang, Wei Cheng, Dongkai Li, Guoyu Zhao, Xianli Lei, Na Cui

“…We assessed the clinical characteristics and lymphocyte subsets at the onset of IAI. A machine‐learning random forest model was used to select important variables, and multivariate logistic regression was used to analyze the factors influencing IAC. …”
Get full text

Socioeconomic and demographic factors associated with anaemia among women of reproductive age in Zimbabwe: a supervised machine learning approach by Garikayi Chemhaka, Elliot Mbunge, Tafadzwa Dzinamarira, Godfrey Musuka, John Batani, Benhildah Muchemwa, Stephen Fashoto, Munyaradzi Mapingure, Rutendo Birri Makota, Ester Petrus

Get full text

Machine‐Learning Based Multi‐Layer Soil Moisture Forecasts—An Application Case Study of the Montana 2017 Flash Drought by J. Du, J. S. Kimball, K. Jencso, Z. Hoylman, C. Brust, D. Ketchum, Y. Xu, H. Lu, J. Sheffield

“…The resulting 30‐m daily SM predictions showed strong performance against in situ SM measurements from 4‐, 8‐ and 20‐inch soil layers, and with 1‐ to 2‐week forecast lead times (R > 0.91; RMSE ≤ 0.047 cm3/cm3). The machine‐learning model was subsequently applied to the entire Montana region, and the SM deficit forecasts with both 1‐ and 2‐week lead times successfully depicted onset, progression, and termination phases of the 2017 Montana flash drought, which was not effectively identified from prevailing operational systems. …”
Get full text

Aquatic system assessment of potentially toxic elements in El Manzala Lake, Egypt: A statistical and machine learning approach by Asmaa Nour Aly Al-Falal, Salah Elsayed, Ezzat A. El Fadaly, Aissam Gaagai, Hani Amir Aouissi, Mohamed S. Abd El-baki, Mohamed Hamdy Eid, Abdallah Elshawadfy Elwakeel, Zaher Mundher Yaseen, Osama Elsherbiny, E.I. Eltahir, Mohamed Gad

“…Additionally, six machine learning models, including Multiple Linear Regression (MLR), Decision Tree (DT), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Adaptive Boosting Regression (AdaBoost), and Multilayer Perceptron (MLP), were developed to predict water quality parameters. …”
Get full text

Study on soil moisture estimation using a three-frequency combination of observations integrated with robust estimation and machine learning by Yintao Liu, Chao Ren, Hongjuan Shao

Get full text

Predicting a failure of postoperative thromboprophylaxis in non-small cell lung cancer: A stacking machine learning approach. by Ligang Hao, Junjie Zhang, Yonghui Di, Zheng Qi, Peng Zhang

“…<h4>Results</h4>This study included 362 patients, including 58 (16.0%) with VTE. Based on the multivariable logistic regression analysis, age, platelets, D-dimers, albumin, smoking history, and epidermal growth factor receptor (EGFR) exon 21 mutation were used to develop the nine machine-learning models. …”
Get full text

Integration of Immunometabolic Composite Indices and Machine Learning for Diabetic Retinopathy Risk Stratification: Insights from NHANES 2011 – 2020 by Cui Xuehao, MD, PhD, Wen Dejia, MD, Li Xiaorong, PhD

“…Methods: Immunometabolic indices reflecting insulin resistance, inflammation, and lipid metabolism were evaluated. Multivariate logistic regression models assessed associations with DR, and Bayesian kernel machine regression analyzed nonlinear interactions. …”
Get full text

Composite dietary antioxidant index and HPV infection from single and mixed associations to SHAP-interpreted machine learning predictions by Pei Zhang

“…Additionally, among the nine machine—learning models, the Gradient Boosting Machine (GBM) showed the best predictive performance [area under curve (AUC) = 0.685]. …”
Get full text

Machine learning with decision curve analysis evaluates nutritional metabolic biomarkers for cardiovascular-kidney-metabolic risk: an NHANES analysis by Jun Huang, Jun Huang, Zhuo Liu, Zhuo Liu, WeiPeng Feng, YuanLing Huang, XinChun Cheng

“…The study developed novel indices (RAR, NPAR, SIRI, Homair) and assessed their CKM predictive value through: Multivariable logistic/Cox regression; Restricted cubic splines; Machine learning (XGBoost, LightGBM); Decision curve analysis. …”
Get full text