Machine learning for predicting all-cause mortality of metabolic dysfunction-associated fatty liver disease: a longitudinal study based on NHANES by Xueni Wang, Huihui Chen, Luqiao Wang, Wenguang Sun

“…The data (demographic, behavioral factors and laboratory indicators) were utilized to construct machine learning models (Coxnet, RSF, GBS) after feature selection. Time-dependent AUC, time-dependent brier and C-index were then evaluated the performance of models. …”
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Identification of Diagnostically Relevant Biomarkers in Patients with Coronary Artery Disease by Comprehensive Analysis by Wu Z, Mo S, Huang Z, Zheng B

“…The purpose of this research is to determine the diagnostic value and therapeutic potential of the endoplasmic reticulum stress (ERS) genes in CAD.Methods: The clinical information and RNA sequence data were obtained from the GEO database and subsequently subjected to a series of optimization and visualization processes using various analytical techniques, including WGCNA, LASSO, SVM-RFE feature selection, random forest (RF), and XGBoost, as well as R software and Cytoscape. …”
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Multi-Objective Scheduling for Green Flexible Assembly Job-Shop System via Multi-Agent Deep Reinforcement Learning With Game Theory by Xiao Wang, Zhongyuan Liang, Peisi Zhong, Dongmin Li, Hongqi Li, Mei Liu

“…A mathematical model is formulated to describe the FAJS problem, which then is translated into a Markov Decision Process (MDP) where an agent directly selects behavioral policies according to the processing state of the current decision point. …”
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RadiomiX for Radiomics Analysis: Automated Approaches to Overcome Challenges in Replicability by Harel Kotler, Luca Bergamin, Fabio Aiolli, Elena Scagliori, Angela Grassi, Giulia Pasello, Alessandra Ferro, Francesca Caumo, Gisella Gennaro

“…<b>Methods</b>: RadiomiX systematically tests classifier and feature selection method combinations known to be suitable for radiomic datasets to determine the best-performing configuration across multiple train–test splits and K-fold cross-validation. …”
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Exploring the capabilities of hyperspectral remote sensing for soil texture evaluation by Mohammad Hosseinpour-Zarnaq, Mahmoud Omid, Fereydoon Sarmadian, Hassan Ghasemi-Mobtaker, Reza Alimardani, Pouya Bohlol

“…Additionally, we compared the performance of random forest (RF) algorithms with partial least squares regression (PLSR), multiple linear regression (MLR), support vector machine regression (SVR), decision trees (DTs), and multilayer perceptron (MLP) neural networks, addressing the effects of feature selection and irregular soil data on the modeling procedure. …”
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Novel integrative models to predict the severity of inflammation and fibrosis in patients with drug-induced liver injury by Yue Zhang, Chuan Lu, Jingying Xu, Qiqi Ma, Mei Han, Li Ying

“…Backward stepwise regression, best subset and logistic regression models were employed for feature selection and model building. Prediction models were presented with nomogram and evaluated by AUC, Brier score, calibration curves and decision curve analysis (DCA).ResultsFor diagnosing moderate–severe inflammation and fibrosis, we calculated the AUC of gamma-glutamyl transpeptidase-to-platelet ratio (GPR), aspartate aminotransferase-to-platelet ratio index (APRI), fibrosis-4 index (FIB-4) and fibrosis-5 index (FIB-5), which were 0.708 and 0.676, 0.778 and 0.667, 0.822 and 0.742, 0.831 and 0.808, respectively. …”
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Elucidating the role of KCTD10 in coronary atherosclerosis: Harnessing bioinformatics and machine learning to advance understanding by Xiaomei Hu, Fanqi Liang, Man Zheng, Juying Xie, Shanxi Wang

“…Advanced analytical tools, including Lasso regression and Support Vector Machine-Recursive Feature Elimination (SVM-RFE), were employed to refine our gene selection. …”
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Diabetes: Non-Invasive Blood Glucose Monitoring Using Federated Learning with Biosensor Signals by Narmatha Chellamani, Saleh Ali Albelwi, Manimurugan Shanmuganathan, Palanisamy Amirthalingam, Anand Paul

“…Adaptive cycle-based segmentation (ACBS) is then used for signal segmentation, followed by particle swarm optimization (PSO) for feature selection, optimizing classification accuracy. …”
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Date delivery scheme of delay-tolerant mobile sensor networks for high-voltage power transmission line inspection robot by FAN Fei, WU Gong-ping, WANG Man, XU Qing-shan, CAO Qi, YANG Song

“…This method aims to realize the remote data collection of inspection robot by delay-tolerant mobile sensor networks for inspection robot (DTMSNR), which is featured by nodes heterogeneity, sparse sensor fields, random mobility, intermittent connectivity, and delay tolerance. …”
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Developing a novel hybrid model based on GRU deep neural network and Whale optimization algorithm for precise forecasting of river’s streamflow by Amin Gharehbaghi, Redvan Ghasemlounia, Farshad Ahmadi, Rasoul Mirabbasi, Ali Torabi Haghighi

“…The Pearson’s correlation coefficient (PCC) and Cosine Amplitude Sensitivity (CAS) as feature (input) selection process determine the only precipitation (P m ) as the most effective input variable among a list of on-site potential climate time series parameters recorded in the study area. …”
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5-Methylcytosine methylation predicts cervical cancer prognosis, shaping immune cell infiltration by Luyang Su, Ren Xu, Yanan Ren, Shixia Zhao, Liyun Song, Cuiqiao Meng, Weilan Liu, Xuan Zhou, Zeqing Du

“…Subsequently, we employed stepwise regression and Least Absolute Shrinkage and Selection Operator Cox regression to quantify 5-methylcytosine modification patterns in patients with cervical squamous cell carcinoma and endocervical adenocarcinoma, yielding the 5-methylcytosine score. …”
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Mapping Soil Organic Matter in Black Soil Cropland Areas Using Remote Sensing and Environmental Covariates by Yu Zhang, Chong Luo, Wenqi Zhang, Zexin Wu, Deqiang Zang

“…These findings underscore the importance of selecting appropriate environmental inputs for improving SOM prediction accuracy.…”
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Comprehensive Multi-indicator Prediction Model for Storage Quality of Multi-cultivar Kiwifruit Based on Visible-Near Infrared Spectroscopy by Zizhao LIANG, Xin LI, Pu LIU, Wenqiang GUAN, Ming LI

“…After the use of different preprocessing algorithms, such as first-order derivatives (FD), standard normal variate (SNV), second-order derivatives, convolutional smoothing, and FD+SNV, the data were combined with competitive adaptive reweighted sampling (CARS) for feature wavelength selection. A quality prediction model based on partial least squares (PLS) and multiple linear regression (MLR) was developed for kiwifruit physicochemical indices. …”
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Perception-Based H.264/AVC Video Coding for Resource-Constrained and Low-Bit-Rate Applications by Lih-Jen Kau, Chin-Kun Tseng, Ming-Xian Lee

“…To this end, macroblocks (MBs) containing human faces are detected using the Viola–Jones algorithm, which leverages AdaBoost for feature selection and a cascade of classifiers for fast and accurate detection. …”
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Early Fault Detection in Electro-Pneumatic Actuators using Mathematical Modelling and Machine Learning: A Bottling Company Case Study by Samuel Olufemi Amudipe, Adeyinka Moses Adeoye, Aderonke Oluwaseunfunmi Akinwumi, Rotimi Adedayo Ibikunle, Segun Adebayo

“…Real-time measurement points were validated through a baseline reference and machine learning models based on support vector machines received training data from labelled sets. The application of feature selection methods helped find essential variables to boost performance metrics in models. …”
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IndoHerb: Indonesia medicinal plants recognition using transfer learning and deep learning by Muhammad Salman Ikrar Musyaffa, Novanto Yudistira, Muhammad Arif Rahman, Ahmad Hoirul Basori, Andi Besse Firdausiah Mansur, Jati Batoro

“…The experimental setup featured essential hyperparameters, including the ExponentialLR scheduler with a gamma value of 0.9, a learning rate of 0.001, the Cross-Entropy Loss function, the Adam optimizer, and a training epoch count of 50. …”
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A maChine and deep Learning Approach to predict pulmoNary hyperteNsIon in newbornS with congenital diaphragmatic Hernia (CLANNISH): Protocol for a retrospective study. by Ilaria Amodeo, Giorgio De Nunzio, Genny Raffaeli, Irene Borzani, Alice Griggio, Luana Conte, Francesco Macchini, Valentina Condò, Nicola Persico, Isabella Fabietti, Stefano Ghirardello, Maria Pierro, Benedetta Tafuri, Giuseppe Como, Donato Cascio, Mariarosa Colnaghi, Fabio Mosca, Giacomo Cavallaro

“…Methods of data augmentation and dimensionality reduction (feature selection and extraction) will be employed to increase sample size and avoid overfitting. …”
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Comparative evaluation of machine learning models versus TIMI score in ST-segment-elevation myocardial infarction patients by Mohit D. Gupta, Dixit Goyal, Shekhar Kunal, Manu Kumar Shetty, M.P. Girish, Vishal Batra, Ankit Bansal, Prashant Mishra, Mansavi Shukla, Vanshika Kohli, Akul Chadha, Arisha Fatima, Subrat Muduli, Anubha Gupta, Jamal Yusuf

“…Six ML algorithms (Extra Tree, Random Forest, Multiple Perceptron, CatBoost, Logistic Regression and XGBoost) were used to train and tune the ML model and to determine the predictors of worse outcomes using feature selection. Additionally, the performance of ML models both for in-hospital and 30-day outcomes was compared to that of TIMI score. …”
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Exosome-mediated microglia-astrocyte interactions drive neuroinflammation in Parkinson's disease with Peli1 as a potential therapeutic target by Min Guo, Ao Guan, Minjie Zhang, Haiqing Li, Weishi Liu, Bangsheng Wu, Weiwei Shen, Tongyao You, Suzhen Liang, Qiang Dong, Jintai Yu, Yanxin Zhao, Mei Cui

“…Neuroinflammation is a key feature of Parkinson's disease (PD), characterized by activated microglia and the conversion of astrocytes into the neurotoxic phenotype, exacerbating the neuroinflammation. …”
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Multi-sensor fusion for AI-driven behavior planning in medical applications by Chang Jianming, Qin Yuanyuan, Qin Yuanyuan, Xu Yanling, Li Li, Li Li, Wu Mianhua, Wu Mianhua, Wang Lulu

“…By dynamically weighing sensor inputs and optimizing feature selection, APFN ensures superior decision-making under varying medical conditions.ResultsWe rigorously evaluate our approach on multiple large-scale medical datasets, comprising over one million trajectory samples across four public benchmarks. …”
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