Multivariable Diagnostic Prediction Model to Detect Hormone Secretion Profile From T2W MRI Radiomics with Artificial Neural Networks in Pituitary Adenomas by Begumhan BAYSAL, Mehmet Bilgin ESER, Mahmut Bilal DOGAN, Muhammet Arif KURSUN

“…Predictors were determined as radiomics features (n=851). Feature selection was based on intraclass correlation coefficient, coefficient variance, variance inflation factor, and LASSO regression analysis. …”
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Addressing Label Noise in Colorectal Cancer Classification Using Cross-Entropy Loss and pLOF Methods With Stacking-Ensemble Technique by Ishrat Zahan Tani, Kah Ong Michael Goh, Md Nazmul Islam, Md Tarek Aziz, S. M. Hasan Mahmud, Dip Nandi

“…Fourth, we adopted a random forest–based recursive feature elimination (RF-RFE) feature selection method with various combinations of features to recursively select the most influential ones for accurate predictions. …”
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Enhancing Telemarketing Success Using Ensemble-Based Online Machine Learning by Shahriar Kaisar, Md Mamunur Rashid, Abdullahi Chowdhury, Sakib Shahriar Shafin, Joarder Kamruzzaman, Abebe Diro

“…To address the above issues, this paper proposes an ensemble machine learning model with feature selection and oversampling techniques to identify potential customers more accurately. …”
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Machine learning algorithm for predict the in-hospital mortality in critically ill patients with congestive heart failure combined with chronic kidney disease by Xunliang Li, Zhijuan Wang, Wenman Zhao, Rui Shi, Yuyu Zhu, Haifeng Pan, Deguang Wang

“…Background The objective of this study was to develop and validate a machine learning (ML) model for predict in-hospital mortality among critically ill patients with congestive heart failure (CHF) combined with chronic kidney disease (CKD).Methods After employing least absolute shrinkage and selection operator regression for feature selection, six distinct methodologies were employed in the construction of the model. …”
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Estimation of TP53 mutations for endometrial cancer based on diffusion-weighted imaging deep learning and radiomics features by Lei Shen, Bo Dai, Shewei Dou, Fengshan Yan, Tianyun Yang, Yaping Wu

“…Radiomics features, convolutional neural network-based DL features, and clinical variables were analyzed. Feature selection was performed using Mann-Whitney U test, LASSO regression, and SelectKBest. …”
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The value of multiparametric MRI radiomics and machine learning in predicting preoperative Ki-67 expression level in breast cancer by Yan Lu, Long Jin, Ning Ding, Mengjuan Li, Shengnan Yin, Yiding Ji

“…The T-test and the Least Absolute Shrinkage and Selection Operator Cross-Validation (LASSO CV) were conducted for feature selection. Modelintra, modelperi, modelintra+peri were established by eleven supervised machine learning (ML) algorithms to predict the expression status of Ki-67 in breast cancer and were verified by the validation groups. …”
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Enhanced Fetal Arrhythmia Classification by Non-Invasive ECG Using Cross Domain Feature and Spatial Differences Windows Information by Gede Angga Pradipta, Putu Desiana Wulaning Ayu, Made Liandana, Dandy Pramana Hostiadi

“…Subsequently, a sample expansion was applied using a various-sized window sliding approach to each ARR and normal signal. Second, feature selection was implemented to reduce data dimensionality by selecting features highly relevant to the class labels. …”
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IL-6-Inducing Peptide Prediction Based on 3D Structure and Graph Neural Network by Ruifen Cao, Qiangsheng Li, Pijing Wei, Yun Ding, Yannan Bin, Chunhou Zheng

“…Most existing methods for predicting IL-6-induced peptides use traditional machine learning methods, whose feature selection is based on prior knowledge. In addition, none of these methods take into account the three-dimensional (3D) structure of peptides, which is essential for their functional properties. …”
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Meat analogues: The relationship between mechanical anisotropy, macrostructure, and microstructure by Miek Schlangen, Iris van der Doef, Atze Jan van der Goot, Mathias P. Clausen, Thomas E. Kodger

“…Last, univariate feature selection provided insight into which parameters are most important for selected target features.…”
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Attention-enhanced optimized deep ensemble network for effective facial emotion recognition by Taimoor Khan, Muhammad Yasir, Chang Choi

“…Subsequently, the channel attention module (CAM) and spatial attention module (SAM) are sequentially incorporated in the framework for dominant feature selection. Finally, we integrated fully connected (FC) layers to accurately classify facial emotions (anger, disgust, fear, happy, neutral, sad, and surprise). …”
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Interpretable machine learning-based prediction of 28-day mortality in ICU patients with sepsis: a multicenter retrospective study by Li Shen, Li Shen, Jiaqiang Wu, Jianger Lan, Chao Chen, Yi Wang, Zhiping Li

“…Predictor selection for the final model was conducted using the least absolute shrinkage and selection operator (LASSO) regression analysis and the Boruta feature selection algorithm. Five machine learning algorithms including logistic regression (LR), decision tree (DT), extreme gradient boosting (XGBoost), support vector machine (SVM), and light gradient boosting machine (lightGBM) were employed to construct models using 10-fold cross-validation. …”
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Dust Storm Attenuation Prediction Using a Hybrid Machine Learning Model Based on Measurements in Sudan by Elfatih A. A. Elsheikh, E. I. Eltahir, Abdulkadir Tasdelen, Mosab Hamdan, Md Rafiqul Islam, Mohamed Hadi Habaebi, Aisha H. Abdullah Hashim

“…The proposed model incorporates XGBoost for feature selection and combines Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) layers to capture both short-term and long-term dependencies in meteorological data. …”
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Using XBGoost, an interpretable machine learning model, for diagnosing prostate cancer in patients with PSA < 20 ng/ml based on the PSAMR indicator by Dengke Li, Baoyuan Chang, Qunlian Huang

“…After applying the Synthetic Minority Over-sampling TEchnique class balancing on the training set, multiple machine learning models were constructed by using the Least Absolute Shrinkage and Selection Operator (LASSO) feature selection to identify the significant variables. …”
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Advanced artificial intelligence with federated learning framework for privacy-preserving cyberthreat detection in IoT-assisted sustainable smart cities by Mahmoud Ragab, Ehab Bahaudien Ashary, Bandar M. Alghamdi, Rania Aboalela, Naif Alsaadi, Louai A. Maghrabi, Khalid H. Allehaibi

“…Initially, the AAIFLF-PPCD model utilizes Harris Hawk optimization (HHO)-based feature selection to identify the most related features from the IoT data. …”
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Data-driven multi-fault detection in pipelines utilizing frequency response function and artificial neural networks by Hussein A. M. Hussein, Sharafiz B. Abdul Rahim, Faizal B. Mustapha, Prajindra S. Krishnan, Nawal Aswan B. Abdul Jalil

“…The data underwent preprocessing, including the application of principal component analysis (PCA) for feature selection. The subsequent data processing stage involved the application of an ANN algorithm for pattern recognition to analyze and classify the acquired data, identifying patterns associated with the replicated fault conditions. …”
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Morphological Neuroimaging Biomarkers for Tinnitus: Evidence Obtained by Applying Machine Learning by Yawen Liu, Haijun Niu, Jianming Zhu, Pengfei Zhao, Hongxia Yin, Heyu Ding, Shusheng Gong, Zhenghan Yang, Han Lv, Zhenchang Wang

“…From this feature pool, a hybrid feature selection algorithm combining the F-score and sequential forward floating selection (SFFS) methods was performed to select features. …”
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Utilizing bioinformatics and machine learning to identify CXCR4 gene-related therapeutic targets in diabetic foot ulcers by Hengyan Zhang, Ye Zhou, Heguo Yan, Changxing Huang, Licong Yang, Yangwen Liu

“…Meanwhile, protein-protein interaction (PPI) networks were constructed using STRING to identify core genes. Feature selection methods such as LASSO, SVM-RFE and random forest algorithm were applied to localize possible therapeutic target genes. …”
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A machine learning-based radiomics approach for differentiating patellofemoral osteoarthritis from non-patellofemoral osteoarthritis using Q-Dixon MRI by Liangjing Lyu, Jing Ren, Wenjie Lu, Jingyu Zhong, Yang Song, Yongliang Li, Weiwu Yao

“…Three predictive models were evaluated: a proton density-weighted image model, a fat fraction model, and a merged model. Feature selection was conducted using analysis of variance, and logistic regression was applied for classification. …”
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Analisis Sentimen Maskapai Penerbangan Menggunakan Metode Naive Bayes dan Seleksi Fitur Information Gain by Arif Bijaksana Putra Negara, Hafiz Muhardi, Indira Melinda Putri

“…The method applied for sentiment classification is Naïve Bayes with the Information Gain feature selection. The purpose of this study was to determine the effect of selecting the Information Gain feature on classification accuracy and prove that the Naïve Bayes method with Information Gain can be used for the classification of sentiment analysis. …”
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Exploring machine learning algorithms for predicting fertility preferences among reproductive age women in Nigeria by Zinabu Bekele Tadese, Teshome Demis Nimani, Kusse Urmale Mare, Fetlework Gubena, Ismail Garba Wali, Jamilu Sani

“…Hence, this study aimed to predict the fertility preferences of reproductive age women in Nigeria using state-of-the-art machine learning techniques.MethodsSecondary data analysis from the recent 2018 Nigeria Demographic and Health Survey dataset was employed using feature selection to identify predictors to build machine learning models. …”
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