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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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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Development of a Predictive Model of Occult Cancer After a Venous Thromboembolism Event Using Machine Learning: The CLOVER Study by Anabel Franco-Moreno, Elena Madroñal-Cerezo, Cristina Lucía de Ancos-Aracil, Ana Isabel Farfán-Sedano, Nuria Muñoz-Rivas, José Bascuñana Morejón-Girón, José Manuel Ruiz-Giardín, Federico Álvarez-Rodríguez, Jesús Prada-Alonso, Yvonne Gala-García, Miguel Ángel Casado-Suela, Ana Bustamante-Fermosel, Nuria Alfaro-Fernández, Juan Torres-Macho

“…Both clinically and ML-driven feature selection were performed to identify predictors for occult cancer. …”
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Whole lung radiomic features are associated with overall survival in patients with locally advanced non-small cell lung cancer treated with definitive radiotherapy by Meng Yan, Zhen Zhang, Jia Tian, Jiaqi Yu, Andre Dekker, Dirk de Ruysscher, Leonard Wee, Lujun Zhao

“…Tumor-based radiomic features and whole lung-based radiomic features were extracted from primary tumor and whole lungs (excluding the primary tumor) delineations in planning CT images. Feature selection of radiomic features was done by the least absolute shrinkage (LASSO) method embedded with a Cox proportional hazards (CPH) model with 5-fold cross-internal validation, with 1000 bootstrap samples. …”
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Analisis Perilaku Entitas untuk Pendeteksian Serangan Internal Menggunakan Kombinasi Model Prediksi Memori dan Metode PCA by Rahmat - Budiarto, Yanif Dwi Kuntjoro

“…This study intention is to build a model for analyzing entity behavior using a memory prediction model and uses the principal component analysis (PCA) as a feature selection method and implement it to detect cyber-attacks and anomalies involving insiders. …”
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Establishing a radiomics model using contrast-enhanced ultrasound for preoperative prediction of neoplastic gallbladder polyps exceeding 10 mm by Dong Jiang, Yi Qian, Yijun Gu, Ru Wang, Hua Yu, Zhenmeng Wang, Hui Dong, Dongyu Chen, Yan Chen, Haozheng Jiang, Yiran Li

“…CEUS has a high accuracy rate in diagnosing the benign or malignant nature of gallbladder space-occupying lesions, which can significantly reduce the preoperative waiting time for related examinations and provide more reliable diagnostic information for clinical practice. Results Feature selection via Lasso led to a final LR model incorporating high-density lipoprotein, smoking status, basal width, and Rad_Signature. …”
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Development and Validation of a Cost-Effective Machine Learning Model for Screening Potential Rheumatoid Arthritis in Primary Healthcare Clinics by Wu W, Hu X, Yan L, Li Z, Li B, Chen X, Lin Z, Zeng H, Li C, Mo Y, Wu Y, Wang Q

“…Random Forest (RF) excelled with 96.20% (95% CI 95.39% to 97.02%) accuracy, 96.22% (95% CI 95.40% to 97.03%) specificity, 96.18% (95% CI 95.37% to 97.00%) sensitivity, and 96.20% (95% CI 95.39% to 97.02%) Areas Under Curves (AUC). A meticulous feature selection identified 11 key features for RA screening. …”
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Predicting the risk of heart failure after acute myocardial infarction using an interpretable machine learning model by Qingqing Lin, Qingqing Lin, Wenxiang Zhao, Wenxiang Zhao, Hailin Zhang, Hailin Zhang, Wenhao Chen, Sheng Lian, Qinyun Ruan, Qinyun Ruan, Zhaoyang Qu, Zhaoyang Qu, Yimin Lin, Yimin Lin, Dajun Chai, Dajun Chai, Dajun Chai, Dajun Chai, Xiaoyan Lin, Xiaoyan Lin, Xiaoyan Lin, Xiaoyan Lin

“…For developing a predictive model for HF risk in AMI patients, the least absolute shrinkage and selection operator (LASSO) Regression was used to feature selection, and four ML algorithms including Random Forest (RF), Extreme Gradient Boost (XGBoost), Support Vector Machine (SVM), and Logistic Regression (LR) were employed to develop the model on the training set. …”
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Construction of a prognostic model for gastric cancer based on immune infiltration and microenvironment, and exploration of MEF2C gene function by Si-yu Wang, Yu-xin Wang, Lu-shun Guan, Ao Shen, Run-jie Huang, Shu-qiang Yuan, Yu-long Xiao, Li-shuai Wang, Dan Lei, Yin Zhao, Chuan Lin, Chang-ping Wang, Zhi-ping Yuan

“…Methods Transcriptome sequence data of GC was obtained from The Cancer Genome Atlas (TCGA), the Gene Expression Omnibus (GEO) and PRJEB25780 cohort for subsequent immune infiltration analysis, immune microenvironment analysis, consensus clustering analysis and feature selection for definition and classification of gene M and N. …”
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Using multiomic integration to improve blood biomarkers of major depressive disorder: a case-control studyResearch in context by Amazigh Mokhtari, El Chérif Ibrahim, Arnaud Gloaguen, Claire-Cécile Barrot, David Cohen, Margot Derouin, Hortense Vachon, Guillaume Charbonnier, Béatrice Loriod, Charles Decraene, Ipek Yalcin, Cynthia Marie-Claire, Bruno Etain, Raoul Belzeaux, Andrée Delahaye-Duriez, Pierre-Eric Lutz

“…Third, we implemented an advanced multiomic integration strategy, with covariate correction and feature selection embedded in a cross-validation procedure. …”
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LcProt: Proteomics‐based identification of plasma biomarkers for lung cancer multievent, a multicentre study by Hengrui Liang, Runchen Wang, Ran Cheng, Zhiming Ye, Na Zhao, Xiaohong Zhao, Ying Huang, Zhanpeng Jiang, Wangzhong Li, Jianqi Zheng, Hongsheng Deng, Yu Jiang, Yuechun Lin, Yun Yan, Lei Song, Jie Li, Xin Xu, Wenhua Liang, Jun Liu, Jianxing He

“…An additional 46 participants from external prospective cohort of 735 participants were used for validation. Feature selection was performed using differential expressed protein analysis, area under curve (AUC) evaluation and least absolute shrinkage and selection operator (LASSO) regression. …”
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Assessing Glioblastoma Treatment Response Using Machine Learning Approach Based on Magnetic Resonance Images Radiomics: An Exploratory Study by Amirreza Sadeghinasab, Jafar Fatahiasl, Marziyeh Tahmasbi, Sasan Razmjoo, Mohammad Yousefipour

“…The second‐best performance was observed with the KNN classifier, which achieved an AUC of 0.80 ± 0.17 when trained on the features selected by the forward sequential algorithm. …”
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Selection of geometrical features of nuclei оn fluorescent images of cancer cells by Ya. U. Lisitsa, M. M. Yatskou, V. V. Skakun, P. D. Pavel D. Kryvasheyeu, V. V. Apanasovich

“…The methods of geometric informative features selection of nuclei on fluorescent images of cancer cells are considered. …”
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Ensemble of feature augmented convolutional neural network and deep autoencoder for efficient detection of network attacks by Selvakumar B, Sivaanandh M, Muneeswaran K, Lakshmanan B

“…In FA-CNN, CNN is trained with augmented features selected using Mutual Information. The FA-CNN is ensembled with Deep Autoencoder to design the ensemble of the classifier. …”
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Real‐time recognition of human motions using multidimensional features in ultrawideband biological radar by Jinxiao Zhong, Liangnian Jin, Qiang Mao

“…A multidimensional features long short‐term memory (LSTM) neural network model is presented using multibranch network structure and high‐dimensional radar feature fusion, which can recognise motions of human in real time, even in the presence of occlusions. The features selected for motion recognition including slow time range‐map and slow time Doppler map. …”
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