A comparative machine learning study of schizophrenia biomarkers derived from functional connectivity by Victoria Shevchenko, R. Austin Benn, Robert Scholz, Wei Wei, Carla Pallavicini, Ulysse Klatzmann, Francesco Alberti, Theodore D. Satterthwaite, Demian Wassermann, Pierre-Louis Bazin, Daniel S. Margulies

“…These findings along with the feature selection pipeline proposed here will facilitate future inquiries into the prediction of schizophrenia subtypes and transdiagnostic phenomena.…”
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Comprehensive approach to predictive analysis and anomaly detection for road crash fatalities by Chopparapu Gowthami, S. Kavitha

“…A Random Forest Regression model is trained to estimate the number of deaths arising from traffic crashes after data preprocessing, which includes feature selection and encoding. The accuracy and predictive power of the model are assessed through the utilization of the Mean Squared Error measure. …”
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Development of a machine learning model related to explore the association between heavy metal exposure and alveolar bone loss among US adults utilizing SHAP: a study based on NHAN... by Jiayi Chen

“…Methods Data were collected from National Health and Nutrition Examination Survey (NHANES) between 2015 and 2018 to develop a machine learning (ML) model. Feature selection was performed using the Least Absolute Shrinkage and Selection Operator (LASSO) regression with 10-fold cross-validation. …”
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Privacy-preserving approach for IoT networks using statistical learning with optimization algorithm on high-dimensional big data environment by Fatma S. Alrayes, Mohammed Maray, Asma Alshuhail, Khaled Mohamad Almustafa, Abdulbasit A. Darem, Ali M. Al-Sharafi, Shoayee Dlaim Alotaibi

“…Besides, the sand cat swarm optimizer (SCSO)-based feature selection (FS) process is employed to decrease the high dimensionality problem. …”
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A preoperative predictive model based on multi-modal features to predict pathological complete response after neoadjuvant chemoimmunotherapy in esophageal cancer patients by Yana Qi, Yanran Hu, Chengting Lin, Ge Song, Liting Shi, Hui Zhu

“…Radiomics features were extracted from contrast-enhanced CT images using PyrRadiomics, while pathomics features were derived from whole-slide images (WSIs) of pathological specimens using a fine-tuned deep learning model (ResNet-50). After feature selection, three single-modality prediction models and a combined multi-modality model integrating two radiomics features, 11 pathomics features, and two clinicopathological features were constructed using the support vector machine (SVM) algorithm. …”
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Spectral Analysis of Light-Adapted Electroretinograms in Neurodevelopmental Disorders: Classification with Machine Learning by Paul A. Constable, Javier O. Pinzon-Arenas, Luis Roberto Mercado Diaz, Irene O. Lee, Fernando Marmolejo-Ramos, Lynne Loh, Aleksei Zhdanov, Mikhail Kulyabin, Marek Brabec, David H. Skuse, Dorothy A. Thompson, Hugo Posada-Quintero

“…In a series of ERGs collected in ASD (<i>n</i> = 77), ADHD (<i>n</i> = 43), ASD + ADHD (<i>n</i> = 21), and control (<i>n</i> = 137) groups, this analysis explores the use of machine learning and feature selection techniques to improve the classification between these clinically defined groups. …”
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Automatic Recognition of Authors Identity in Persian based on Systemic Functional Grammar by Fatemeh Soltanzadeh, Azadeh Mirzaei, Mohammad Bahrani, Shahram Modarres Khiabani

“…The combined use of function words and SFG methods achieved an accuracy of 74.47% for Persian author identification. Subsequent feature selection identified the most effective features for the machine learning phase. …”
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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

“…From these genes, 45 machine learning models were constructed using different combinations of feature selection methods and classification algorithms. …”
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Comprehensive Sepsis Risk Prediction in Leukemia Using a Random Forest Model and Restricted Cubic Spline Analysis by Kou Y, Tian Y, Ha Y, Wang S, Sun X, Lv S, Luo B, Yang Y, Qin L

“…This study aimed to develop and validate a predictive model for sepsis risk in leukemia patients using machine learning techniques.Methods: This retrospective study included 4310 leukemia patients admitted to the Affiliated Hospital of Guangdong Medical University from 2005 to 2024, using 70% for training and 30% for validation. Feature selection was performed using univariate logistic regression, LASSO, and the Boruta algorithm, followed by multivariate logistic regression analysis. …”
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Explainable machine learning model for predicting decline in platelet count after interventional closure in children with patent ductus arteriosus by Song-Yue Zhang, Yi-Dong Zhang, Hao Li, Qiao-Yu Wang, Qiao-Fang Ye, Xun-Min Wang, Tian-He Xia, Yue-E He, Xing Rong, Ting-Ting Wu, Rong-Zhou Wu

“…The extra tree algorithm was used for feature selection and four ML algorithms [random forest (RF), adaptive boosting, extreme gradient boosting, and logistic regression] were established. …”
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Automatic segmentation model and machine learning model grounded in ultrasound radiomics for distinguishing between low malignant risk and intermediate-high malignant risk of adnex... by Lu Liu, Wenjun Cai, Feibo Zheng, Hongyan Tian, Yanping Li, Ting Wang, Xiaonan Chen, Wenjing Zhu

“…Radiomics features were extracted utilizing a feature analysis system in Pyradiomics. Feature selection was conducted using the Spearman correlation analysis, Mann–Whitney U-test, and least absolute shrinkage and selection operator (LASSO) regression. …”
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Sistem Pakar Penentuan Penggunaan Bahan Tambahan Pangan untuk Produk Pangan by Melva Linda Aritonang, Kudang Boro Seminar, Nugraha Edhi Suyatma, Irman Hermadi

“…The method used is a decision tree with C5.0 algorithm to classify types of food categories with parameters in the form of basic ingredients and ways of processing food products. Feature selection with information gain results that mixing is a processing method that is quite influential on the decision tree model with maximum information gain value. …”
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Machine Learning–Based Risk Factor Analysis and Prediction Model Construction for the Occurrence of Chronic Heart Failure: Health Ecologic Study by Qian Xu, Xue Cai, Ruicong Yu, Yueyue Zheng, Guanjie Chen, Hui Sun, Tianyun Gao, Cuirong Xu, Jing Sun

“…Principal component analysis and random forest (RF) were used as feature selection techniques. Subsequently, several ML models, namely decision tree, RF, extreme gradient boosting, adaptive boosting (AdaBoost), support vector machine, naive Bayes model, multilayer perceptron, and bootstrap forest, were constructed, and their performance was evaluated. …”
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Radiomics for Predicting the Development of Brain Edema from Normal-Appearing Early Brain-CT After Cardiac Arrest and Return of Spontaneous Circulation by Michael Scheschenja, Eva-Marie Müller-Stüler, Simon Viniol, Joel Wessendorf, Moritz B. Bastian, Jarmila Jedelská, Alexander M. König, Andreas H. Mahnken

“…Radiomics features were calculated using Pyradiomics (v3.0.1) in 3DSlicer (v5.2.2). Feature selection involved reproducibility analysis via ICC and LASSO regression, retaining five features per segmentation method. …”
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Hippocampal Functional Radiomic Features for Identification of the Cognitively Impaired Patients from Low-Back-Related Pain: A Prospective Machine Learning Study by Yang Z, Liang X, Ji Y, Zeng W, Wang Y, Zhang Y, Zhou F

“…After calculating the amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo), voxel-mirrored homotopic connectivity (VMHC) and degree centrality (DC) imaging, the radiomic features (n = 819) of bilateral hippocampi were extracted from these images, respectively. After feature selection, machine learning models were trained. …”
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Forecasting the Incidence of Mumps Based on the Baidu Index and Environmental Data in Yunnan, China: Deep Learning Model Study by Xin Xiong, Linghui Xiang, Litao Chang, Irene XY Wu, Shuzhen Deng

“…MethodsWe analyzed mumps incidence in Yunnan Province from 2014 to 2023, and used time series data, including mumps incidence, Baidu search index, and environmental factors, from 2016 to 2023, to develop predictive models based on long short-term memory networks. Feature selection was conducted using Pearson correlation analysis, and lag correlations were explored through a distributed nonlinear lag model (DNLM). …”
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Ultrasound-based radiomics and clinical factors-based nomogram for early intracranial hypertension detection in patients with decompressive craniotomy by Zunfeng Fu, Lin Peng, Laicai Guo, Chao Qin, Yanhong Yu, Jiajun Zhang, Yan Liu

“…Radiomics features were extracted from ONS images, and feature selection methods were applied to construct predictive models using logistic regression (LR), support vector machine (SVM), random forest (RF), and K-Nearest Neighbors (KNN). …”
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Construction and evaluation of a triage assessment model for patients with acute non-traumatic chest pain: mixed retrospective and prospective observational study by Xuan Zhou, Gangren Jian, Yuefang He, Yating Huang, Jie Zhang, Shengfang Wang, Yunxian Wang, Ruofei Zheng

“…Methods After data preprocessing and feature selection, univariate and multiple logistic regression analyses were performed to identify potential predictors associated with acute non-traumatic chest pain. …”
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Exploring the assessment of post-cardiac valve surgery pulmonary complication risks through the integration of wearable continuous physiological and clinical data by Lixuan Li, Yuekong Hu, Zhicheng Yang, Zeruxin Luo, Jiachen Wang, Wenqing Wang, Xiaoli Liu, Yuqiang Wang, Yong Fan, Pengming Yu, Zhengbo Zhang

“…We systematically evaluated three different input types (physiological, clinical, and both) and five classifiers (XGB, LR, RF, SVM, KNN) to identify the combination with strong predictive performance for PPCs. Feature selection was conducted using Recursive Feature Elimination with Cross-Validated (RFECV) for each model, yielding an optimal feature subset for each, followed by a grid search to tune hyperparameters. …”
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Mathematical modelling-based blockchain with attention deep learning model for cybersecurity in IoT-consumer electronics by Hayam Alamro, Mohammed Maray, Jawhara Aljabri, Saad Alahmari, Monir Abdullah, Jehad Saad Alqurni, Faiz Abdullah Alotaibi, Abdelmoneim Ali Mohamed

“…The MGOADL-CS technique uses an improved tunicate swarm algorithm (ITSA) based feature selection approach for dimensionality reduction. …”
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