PCA and PSO based optimized support vector machine for efficient intrusion detection in internet of things by Mutkule Prasad Raghunath, Shyam Deshmukh, Poonam Chaudhari, Sunil L. Bangare, Kishori Kasat, Mohan Awasthy, Batyrkhan Omarov, Rajesh R. Waghulde

“…This article presents the development of an intrusion detection system for the Internet of Things using machine learning and feature selection techniques. The system aims to accurately categorise and forecast attacks on IoT devices. …”
Get full text

Comparison of time-to-event machine learning models in predicting biliary complication and mortality rate in liver transplant patients by Aref Andishgar, Sina Bazmi, Kamran B. Lankarani, Seyed Alireza Taghavi, Mohammad Hadi Imanieh, Gholamreza Sivandzadeh, Samira Saeian, Nazanin Dadashpour, Alireza Shamsaeefar, Mahdi Ravankhah, Hamed Nikoupour Deylami, Reza Tabrizi, Mohammad Hossein Imanieh

“…Survival analysis used filter (Cox-P, Cox-c) and embedded (RSF, LASSO) feature selection methods. Seven survival machine learning algorithms were used: LASSO, Ridge, RSF, E-NET, GBS, C-GBS, and FS-SVM. …”
Get full text

Multivariate description of gait changes in a mouse model of peripheral nerve injury and trauma. by Bilal A Naved, Shuling Han, Kyle M Koss, Mary J Kando, Jiao-Jing Wang, Craig Weiss, Maya G Passman, Jason A Wertheim, Yuan Luo, Zheng J Zhang

“…Multivariate relationships among the 30+ spatiotemporal measures were evaluated using exploratory factor analysis and forward feature selection to identify the features and latent factors that best described gait phenotypes. …”
Get full text

Classification of Epileptic Seizures by Simple Machine Learning Techniques: Application to Animals’ Electroencephalography Signals by Illia Pidvalnyi, Anna Kostenko, Oleksandr Sudakov, Dmytro Isaev, Oleksandr Maximyuk, Oleg Krishtal, Olena Iegorova, Ievgen Kabin, Zoya Dyka, Steffen Ortmann, Peter Langendorfer

“…A principal component analysis was applied for feature selection before using a support vector machine for the detection of seizures. …”
Get full text

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.…”
Get full text

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. …”
Get full text

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. …”
Get full text

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. …”
Get full text

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. …”
Get full text

A robust multimodal brain MRI-based diagnostic model for migraine: validation across different migraine phases and longitudinal follow-up data by Jong Young Namgung, Eunchan Noh, Yurim Jang, Mi Ji Lee, Bo-yong Park

“…We employed a regularization-based feature selection method combined with a random forest classifier to construct a diagnostic model. …”
Get full text

Interpretable machine learning model for predicting clinically significant prostate cancer: integrating intratumoral and peritumoral radiomics with clinical and metabolic features by Wenjun Zhao, Mengyan Hou, Juan Wang, Dan Song, Yongchao Niu

“…Radiomic features were extracted separately from the VOIintra and VOIperi. After feature selection via the recursive feature elimination (RFE) algorithm, intratumoral radiomic score (intra-rad-score) and peritumoral radiomic score (peri-rad-score) were constructed. …”
Get full text

Association Between Artificial Liver Support System and Prognosis in Hepatitis B Virus-Related Acute-on-Chronic Liver Failure by Cui K, Liu CH, Teng X, Chen F, Xu Y, Zhou S, Yang Q, Du L, Ma Y, Bai L

“…Kaplan-Meier survival analysis curves show the 28-day, 60-day and 90-day transplant-free mortality. Based on the feature selection result of univariate logistic, univariate Cox and Boruta algorithm, the univariate and multivariate logistic and COX regression models were used to investigate the association of ALSS with 28-day, 60-day and 90-day outcomes in patients with HBV-ACLF. …”
Get full text

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. …”
Get full text

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. …”
Get full text

Cardiac computer tomography-derived radiomics in assessing myocardial characteristics at the connection between the left atrial appendage and the left atrium in atrial fibrillation... by Xiao-Xuan Wei, Cai-Ying Li, Hai-Qing Yang, Peng Song, Bai-Lin Wu, Fang-Hua Zhu, Jing Hu, Xiao-Yu Xu, Xin Tian

“…The radiomics model was built by extracting radiomic features of the myocardial tissue using Pyradiomics, and employing Least absolute shrinkage and selection operator (LASSO) method for feature selection, combining random forest with support vector machine (SVM) classifier.ResultsThere were 82 cases in the AF group [44 males, 65.00 (59, 70)], and 56 cases in the control group (21 males, 61.09 ± 7.18). …”
Get full text

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. …”
Get full text

Machine learning-based analyses of contributing factors for the development of hypertension: a comparative study by Marenao Tanaka, Yukinori Akiyama, Kazuma Mori, Itaru Hosaka, Keisuke Endo, Toshifumi Ogawa, Tatsuya Sato, Toru Suzuki, Toshiyuki Yano, Hirofumi Ohnishi, Nagisa Hanawa, Masato Furuhashi

“…Among the 58 parameters, systolic blood pressure, age and FLI were identified as important candidates by random forest feature selection with 10-fold cross-validation. The AUCs of ML models were 0.765–0.825, and discriminatory capacity was significantly improved in the artificial neural network model compared to that in the logistic regression model.Conclusions The development of hypertension can be simply and accurately predicted by each ML model using systolic blood pressure, age and FLI as selected features. …”
Get full text

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. …”
Get full text

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. …”
Get full text

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. …”
Get full text