Predictive model of acute kidney injury in critically ill patients with acute pancreatitis: a machine learning approach using the MIMIC-IV database by Shengwei Lin, Wenbin Lu, Ting Wang, Ying Wang, Xueqian Leng, Lidan Chi, Peipei Jin, Jinjun Bian

“…This study aims to develop and validate predictive models using machine learning (ML) to identify the onset of AKI in patients with AP.Methods Patients with AP were extracted from the MIMIC-IV database. We performed feature selection using the random forest method. Model construction involved an ensemble of ML, including random forest (RF), support vector machine (SVM), k-nearest neighbors (KNN), naive Bayes (NB), neural network (NNET), generalized linear model (GLM), and gradient boosting machine (GBM). …”
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Construction of a poor prognosis prediction and visualization system for intracranial aneurysm endovascular intervention treatment based on an improved machine learning model by Chunyu Lei, Anhui Fu, Bin Li, Shengfu Zhou, Jun Liu, Yu Cao, Bo Zhou

“…This model simultaneously performed feature selection and weight determination. Logistic multivariate analysis was used to validate the selected features. …”
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Potential biomarkers and immune infiltration linking endometriosis with recurrent pregnancy loss based on bioinformatics and machine learning by Jianhui Chen, Qun Li, Xiaofang Liu, Fang Lin, Yaling Jing, Jiayan Yang, Lianfang Zhao

“…Moreover, immune cell infiltration was estimated using CIBERSORTx, and the Cancer Genome Atlas (TCGA) database was employed to elucidate the role of key genes in endometrial carcinoma (EC).Results26 common differentially expressed genes (DEGs) were screened in both diseases, three of which were identified as common core genes (MAN2A1, PAPSS1, RIBC2) through the combination of WGCNA, PPI network, and machine learning-based feature selection. The area under the curve (AUC) values generated by the ROC indicates excellent diagnostic powers in both EMs and RPL. …”
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A Pervasive Approach to EEG-Based Depression Detection by Hanshu Cai, Jiashuo Han, Yunfei Chen, Xiaocong Sha, Ziyang Wang, Bin Hu, Jing Yang, Lei Feng, Zhijie Ding, Yiqiang Chen, Jürg Gutknecht

“…Then, the minimal-redundancy-maximal-relevance feature selection technique reduced the dimensionality of the feature space. …”
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Study on influencing factors of age-adjusted Charlson comorbidity index in patients with Alzheimer's disease based on machine learning model by Jian Ding, Jian Ding, Zheng Long, Yiming Liu, Min Wang

“…The model performance is evaluated through classification accuracy, net benefit, and robustness. The feature selection results were validated by regression analysis.ResultsMultiple models have performed well in classifying aCCI patients, among which the model constructed using LASSO regression screening feature factors has the best performance, with the highest classification accuracy and net benefit. …”
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The combined effectiveness of acoustic indices in measuring bird species richness in biodiverse sites in Cyprus, China, and Australia by Christos Mammides, Pan Wuyuan, Guohualing Huang, Rachakonda Sreekar, Christina Ieronymidou, Aiwu Jiang, Eben Goodale, Harris Papadopoulos

“…Using the Boruta feature selection algorithm and random forest regressors, we find that the effectiveness of the indices varies considerably across study areas, and it is generally lower than what would be required to monitor bird species richness accurately (R2Cyprus = 0.06, R2China = 0.31, R2Australia = 0.52). …”
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Construction and validation of risk prediction models for renal replacement therapy in patients with acute pancreatitis by Fei Zuo, Lei Zhong, Jie Min, Jinyu Zhang, Longping Yao

“…Acute kidney injury (AKI) was observed in 52.43% of patients with AP, and 9.05% required RRT. After feature selection, four of 41 clinical factors were ultimately chosen for use in model construction. …”
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Predicting the exposure of mycophenolic acid in children with autoimmune diseases using a limited sampling strategy: A retrospective study by Ping Zheng, Ting Pan, Ya Gao, Juan Chen, Liren Li, Yan Chen, Dandan Fang, Xuechun Li, Fei Gao, Yilei Li

“…Univariate analysis was applied for feature selection. Ten algorithms, including Random Forest, XGBoost, LightGBM, Gradient Boosting Decision Tree, CatBoost, Artificial Neural Network, Grandient Boosting Machine, Transformer, Wide&Deep, and TabNet, were employed for modeling based on two, three, or four concentrations of MPA. …”
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A Study on Canopy Volume Measurement Model for Fruit Tree Application Based on LiDAR Point Cloud by Na Guo, Ning Xu, Jianming Kang, Guohai Zhang, Qingshan Meng, Mengmeng Niu, Wenxuan Wu, Xingguo Zhang

“…Additionally, improvements to the mesh integral volume method incorporate the effects of canopy gaps in height difference calculations, significantly enhancing the accuracy of canopy volume estimation. For feature selection, a random forest-based recursive feature elimination method with cross-validation was employed to filter 10 features. …”
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Machine learning-based prediction of in-hospital mortality for critically ill patients with sepsis-associated acute kidney injury by Tianyun Gao, Zhiqiang Nong, Yuzhen Luo, Manqiu Mo, Zhaoyan Chen, Zhenhua Yang, Ling Pan

“…Ensemble stepwise feature selection method was used to screen for effective features. …”
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Radiomics Features Based on MRI-ADC Maps of Patients with Breast Cancer: Relationship with Lesion Size, Features Stability, and Model Accuracy by Begumhan BAYSAL, Hakan BAYSAL, Mehmet Bilgin ESER, Mahmut Bilal DOGAN, Orhan ALIMOGLU

“…Stability of radiomics features (n=851) was evaluated with intraclass correlation coefficient (ICC, >0.75) and coefficient of variation (CoV, <0.15). Feature selection was made with variance inflation factor (VIF, <10) and least absolute shrinkage and selection operator regression. …”
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Identifying Optimal Variables to Predict Soil Organic Carbon in Sandy, Saline, and Black Soil Regions: Remote Sensing, Terrain, or Climate Factors? by Liping Wang, Huanjun Liu, Xiang Wang, Xiaofeng Xu, Liyuan He, Chong Luo, Yong Li, Xinle Zhang, Deqiang Zang, Shufeng Zheng, Xiaodan Mei

“…To address this issue, we used the principal component analysis (PCA) method for the feature selection of bands, spectral indexes, and terrain factors for each region. …”
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A maChine and deep Learning Approach to predict pulmoNary hyperteNsIon in newbornS with congenital diaphragmatic Hernia (CLANNISH): Protocol for a retrospective study. by Ilaria Amodeo, Giorgio De Nunzio, Genny Raffaeli, Irene Borzani, Alice Griggio, Luana Conte, Francesco Macchini, Valentina Condò, Nicola Persico, Isabella Fabietti, Stefano Ghirardello, Maria Pierro, Benedetta Tafuri, Giuseppe Como, Donato Cascio, Mariarosa Colnaghi, Fabio Mosca, Giacomo Cavallaro

“…Methods of data augmentation and dimensionality reduction (feature selection and extraction) will be employed to increase sample size and avoid overfitting. …”
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Optimasi Klasifikasi Sentimen Komentar Pengguna Game Bergerak Menggunakan Svm, Grid Search Dan Kombinasi N-Gram by Syahroni Wahyu Iriananda, Renaldi Widi Budiawan, Aviv Yuniar Rahman, Istiadi Istiadi

“…In this study, sentiment classification was performed using the Support Vector Machine (SVM) algorithm, employing N-Gram techniques for feature selection. Grid Search (GS) was utilized for hyperparameter optimization to achieve the highest possible accuracy. …”
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Characterization and Selection of WiFi Channel State Information Features for Human Activity Detection in a Smart Public Transportation System by Roya Alizadeh, Yvon Savaria, Chahe Nerguizian

“…Since the environment of a transportation system changes dynamically and non-deterministically, we propose analyzing these changes with a heuristic algorithm that leverages a decision tree to automate a decision-making solution for feature selection. Principal Component Analysis (PCA) is performed before the decision tree algorithm. …”
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Characterization of microbiota signatures in Iberian pig strains using machine learning algorithms by Lamiae Azouggagh, Noelia Ibáñez-Escriche, Marina Martínez-Álvaro, Luis Varona, Joaquim Casellas, Sara Negro, Cristina Casto-Rebollo

“…ML models, particularly CB and RF, as well as SVM in certain scenarios, combined with a feature selection process, effectively classified genetic groups based on microbiome data and identified key microbial taxa. …”
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Characterisation of cardiovascular disease (CVD) incidence and machine learning risk prediction in middle-aged and elderly populations: data from the China health and retirement lo... by Qing Huang, Zihao Jiang, Bo Shi, Jiaxu Meng, Li Shu, Fuyong Hu, Jing Mi

“…Data preprocessing included missing value imputation via random forest. Feature selection was performed using the Least Absolute Shrinkage and Selection Operator (Lasso CV) method with cross-validation prior to model training. …”
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A machine learning model accurately identifies glycogen storage disease Ia patients based on plasma acylcarnitine profiles by Joost Groen, Bas M. de Haan, Ruben J. Overduin, Andrea B. Haijer-Schreuder, Terry GJ Derks, M. Rebecca Heiner-Fokkema

“…Our approach included hyperparameter tuning and feature selection and generalization was checked using both nested cross-validation and a held-out test set. …”
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Radiomic prediction for durable response to high‐dose methotrexate‐based chemotherapy in primary central nervous system lymphoma by Haoyi Li, Mingming Xiong, Ming Li, Caixia Sun, Dao Zheng, Leilei Yuan, Qian Chen, Song Lin, Zhenyu Liu, Xiaohui Ren

“…Multiple machine‐learning algorithms were utilized for feature selection and classification to build a radiomic signature. …”
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A model-based factorization method for scRNA data unveils bifurcating transcriptional modules underlying cell fate determination by Jun Ren, Ying Zhou, Yudi Hu, Jing Yang, Hongkun Fang, Xuejing Lyu, Jintao Guo, Xiaodong Shi, Qiyuan Li

“…In summary, MGPfactXMBD offers a manifold-learning framework in scRNA-seq data which enables feature selection for specific biological processes and contributing to advance our understanding of biological determination of cell fate.…”
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