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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An EEG-based framework for automated discrimination of conversion to Alzheimer’s disease in patients with amnestic mild cognitive impairment: an 18-month longitudinal study by Yingfeng Ge, Jianan Yin, Caie Chen, Shuo Yang, Yuduan Han, Chonglong Ding, Jiaming Zheng, Yifan Zheng, Jinxin Zhang

“…Spectral, nonlinear, and functional connectivity features were extracted from the EEG data, subjected to feature selection and dimensionality reduction, and then fed into various machine learning classifiers for discrimination. …”
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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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Analisis Kredit Pembayaran Biaya Kuliah Dengan Pendekatan Pembelajaran Mesin by Arliyanti Nurdin, Rizqa Amelia Zunaidi, Muhammad Arkan Fauzan Wicaksono, Agi Lobita Japtara Martadinata

“…The system design stage consists of preprocessing, feature selection, modeling, uji and evaluation of results. …”
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AI-based analysis of fetal growth restriction in a prospective obstetric cohort quantifies compound risks for perinatal morbidity and mortality and identifies previously unrecogniz... by Raquel M. Zimmerman, Edgar J. Hernandez, Mark Yandell, Martin Tristani-Firouzi, Robert M. Silver, William Grobman, David Haas, George Saade, Jonathan Steller, Nathan R. Blue

“…Performance was assessed as area under the receiver-operating characteristics curve (AUC). Results Feature selection identified the 16 most informative variables, which yielded a PGM with good overall performance in the validation cohort (AUC 0.83, 95% CI 0.79–0.87), including among “N of 1” unique scenarios (AUC 0.81, 0.72–0.90). …”
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Radiogenomics and machine learning predict oncogenic signaling pathways in glioblastoma by Abdul Basit Ahanger, Syed Wajid Aalam, Tariq Ahmad Masoodi, Asma Shah, Meraj Alam Khan, Ajaz A. Bhat, Assif Assad, Muzafar Ahmad Macha, Muzafar Rasool Bhat

“…Dimensionality reduction and feature selection were applied and Data imbalance was addressed with SMOTE. …”
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Prediction of microvascular obstruction from angio-based microvascular resistance and available clinical data in percutaneous coronary intervention: an explainable machine learning... by Zhe Zhang, Yang Dai, Peng Xue, Xue Bao, Xinbo Bai, Shiyang Qiao, Yuan Gao, Xuemei Guo, Yanan Xue, Qing Dai, Biao Xu, Lina Kang

“…The ML workflow comprised feature selection using the Boruta algorithm, model construction with seven classifiers, hyperparameter optimization via ten-fold cross-validation, model comparison based on the area under the curve (AUC), and a Shapley additive explanations (SHAP) analysis to analyze the significance of different features. 32.29% of patients showed inconsistency between AMR and MVO, but we successfully constructed a predictive model for MVO. …”
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Integrating Two-Tier Optimization Algorithm With Convolutional Bi-LSTM Model for Robust Anomaly Detection in Autonomous Vehicles by Moneerah Alotaibi, Manal Abdullah Alohali, Khalid Mahmood, Asma A. Alhashmi, Jehad Saad Alqurni, Sultan Refa Alotaibi, Ahmad A. Alzahrani, Imene Issaoui

“…This article develops an improved whale optimization algorithm-based feature selection using explainable artificial intelligence for robust anomaly detection (IWOAFS-XAIAD) technique in autonomous driving. …”
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Correlation between the white blood cell/platelet ratio and 28-day all-cause mortality in cardiac arrest patients: a retrospective cohort study based on machine learning by Huai Huang, Guangqin Ren, Shanghui Sun, Zhi Li, Yongtian Zheng, Lijuan Dong, Shaoliang Zhu, Xiaosheng Zhu, Wenyu Jiang

“…ObjectiveThis study aims to evaluate the association between the white blood cell-to-platelet ratio (WPR) and 28-day all-cause mortality among patients experiencing cardiac arrest.MethodsUtilizing data from 748 cardiac arrest patients in the Medical Information Mart for Intensive Care-IV (MIMIC-IV) 2.2 database, machine learning algorithms, including the Boruta feature selection method, random forest modeling, and SHAP value analysis, were applied to identify significant prognostic biomarkers. …”
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Quantitative Physiologic MRI Combined with Feature Engineering for Developing Machine Learning-Based Prediction Models to Distinguish Glioblastomas from Single Brain Metastases by Seyyed Ali Hosseini, Stijn Servaes, Brandon Hall, Sourav Bhaduri, Archith Rajan, Pedro Rosa-Neto, Steven Brem, Laurie A. Loevner, Suyash Mohan, Sanjeev Chawla

“…<b>Results</b>: A random forest classifier with ANOVA F-value feature selection algorithm using both interacting and non-interacting features provided the best diagnostic performance in distinguishing GBMs from BMs with an area under the ROC curve of 92.67%, a classification accuracy of 87.8%, a sensitivity of 73.64% and a specificity of 97.5%. …”
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Zipper Pattern: An Investigation into Psychotic Criminal Detection Using EEG Signals by Gulay Tasci, Prabal Datta Barua, Dahiru Tanko, Tugce Keles, Suat Tas, Ilknur Sercek, Suheda Kaya, Kubra Yildirim, Yunus Talu, Burak Tasci, Filiz Ozsoy, Nida Gonen, Irem Tasci, Sengul Dogan, Turker Tuncer

“…The proposed ZPat extracts features by analyzing the relationships between channels. In the feature selection phase of the proposed XFE model, an iterative neighborhood component analysis (INCA) feature selector was used to choose the most distinctive features. …”
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High-throughput untargeted metabolomics reveals metabolites and metabolic pathways that differentiate two divergent pig breeds by S. Bovo, M. Bolner, G. Schiavo, G. Galimberti, F. Bertolini, S. Dall’Olio, A. Ribani, P. Zambonelli, M. Gallo, L. Fontanesi

“…The molecular data were analysed using a bioinformatics pipeline specifically designed for identifying differentially abundant metabolites between the two breeds in a robust and statistically significant manner, including the Boruta algorithm, which is a Random Forest wrapper, and sparse Partial Least Squares Discriminant Analysis (sPLS-DA) for feature selection. After thoroughly evaluating the impact of random components on missing value imputation, 100 discriminant metabolites were selected by Boruta and 17 discriminant metabolites (all included within the previous list) were identified with sPLS-DA. …”
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A Dynamic Adaptive Ensemble Learning Framework for Noninvasive Mild Cognitive Impairment Detection: Development and Validation Study by Aoyu Li, Jingwen Li, Yishan Hu, Yan Geng, Yan Qiang, Juanjuan Zhao

“…To address the challenges (eg, the curse of dimensionality and increased model complexity) posed by high-dimensional features, we developed a dynamic adaptive feature selection optimization algorithm to identify the most impactful subset of features for classification performance. …”
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