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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High-Throughput Phenotyping for Agronomic Traits in Cassava Using Aerial Imaging by José Henrique Bernardino Nascimento, Diego Fernando Marmolejo Cortes, Luciano Rogerio Braatz de Andrade, Rodrigo Bezerra de Araújo Gallis, Ricardo Luis Barbosa, Eder Jorge de Oliveira

“…The best prediction models were observed for the traits of plant vigor and dry matter content, using the Generalized Linear Model with Stepwise Feature Selection (GLMSS) and the K-Nearest Neighbor (KNN) model. …”
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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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Privacy protection risk identification mechanism based on automated feature combination by CAI Minchao, YAO Hongwei, WANG Yang, QIN Zhan, CHEN Shaomeng, REN Kui

“…Therefore, the important features selected automatically were integrated into the recognition model, the first cross-application model of rules and algorithms was designed and implemented. …”
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Traffic anomaly detection method in networks based on improved clustering algorithm by Hong-cheng LI, Xiao-ping WU, Hong-hai JIANG

“…To solve the problem that traditional traffic abnormal detection methods were not accurate enough,a traf-fic anomaly detection method based on improved k-means was proposed.All kinds of network traffic data were pre-processed to make k-means algorithm can apply to enumeration data detection.Then a features selection method was pro-posed with the analysis of the distribution of network traffic data to avoid the distance useless caused by too much fea-tures.Furthermore,the clustering process of K clusters was optimized based on dichotomy,aiming to reduce the effects of initial clusters centers selection.Simulation results demonstrate the effectiveness of the algorithm.…”
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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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Arrhythmia Classification Techniques Using Deep Neural Network by Ali Haider Khan, Muzammil Hussain, Muhammad Kamran Malik

“…The primary concerns that affect the success of the developed arrhythmia detection systems are (i) manual features selection, (ii) techniques used for features extraction, and (iii) algorithm used for classification and the most important is the use of imbalanced data for classification. …”
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Classification of Myopathy and Amyotrophic Lateral Sclerosis Electromyograms Using Bat Algorithm and Deep Neural Networks by A. Bakiya, A. Anitha, T. Sridevi, K. Kamalanand

“…Hence, for computer-aided identification of abnormalities, extraction of features, selection of superlative feature subset, and developing an efficient classifier are indispensable. …”
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Localization of Accessory Pathways in Patients with Wolff-Parkinson-White Syndrome Using Cross-Recurrence Plot of Precordial Leads by Sakineh Yahyazadeh, Nader Jafarnia Dabanloo, Ali Motie Nasrabadi, Alireza Ghorbani Sharif

“…This study was to develop a novel semi-automatic localization of AP in patients with WPW syndrome, using features selected from the cross-recurrence plot (CRP) of consecutive precordial leads on ECG. …”
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Transparent OLED displays for selective bidirectional viewing using ZnO/Yb:Ag cathode with highly smooth and low-barrier surface by Eun-young Choi, Sung-Cheon Kang, Kanghoon Kim, Su-Hyeon Lee, Jeong-Beom Kim, Jang-Kun Song

“…Secondly, we propose a novel TrOLED pixel structure that features selective bidirectional viewing, allowing different types of information to be selectively displayed on each side while preserving overall transparency and minimizing pixel complexity. …”
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