Prediction of prognosis, efficacy of lung adenocarcinoma by machine learning model based on immune and metabolic related genes by Cong Xue, Yi-Zhi Dai, Gui-Long Li, Yi Zhang

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The application of risk models based on machine learning to predict endometriosis‐associated ovarian cancer in patients with endometriosis by Xiaopei Chao, Shu Wang, Jinghe Lang, Jinhua Leng, Qingbo Fan

“…The machine learning‐based risk model performed better than the logistic regression model in DeLong's test (p = 0.036). …”
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Machine learning models for diagnosing lymph node recurrence in postoperative PTC patients: a radiomic analysis by Feng Pang, Lijiao Wu, Jianping Qiu, Yu Guo, Liangen Xie, Shimin Zhuang, Mengya Du, Danni Liu, Chenyue Tan, Tianrun Liu

“…Results This study analyzed 693 lymph nodes (302 positive and 391 negative) and identified 35 significant radiomic features through dimensionality reduction and selection. The three machine learning models, including the Lasso regression, Support Vector Machine (SVM), and RF radiomics models, showed.…”
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Machine learning model for differentiating malignant from benign thyroid nodules based on the thyroid function data by Quan Zhou, Lihua Zhang, Nan Xiang, Lele Zhang, Fuqiang Ma, Fengchang Yu, Shenhui Lv, Zhilin Lu, He-Rong Mao

“…Objectives To develop and validate a machine learning (ML) model to differentiate malignant from benign thyroid nodules (TNs) based on the routine data and provide diagnostic assistance for medical professionals.Setting A qualified panel of 1649 patients with TNs from one hospital were stratified by gender, age, free triiodothyronine (FT3), free thyroxine (FT4) and thyroid peroxidase antibody (TPOAB).Participants Thyroid function (TF) data of 1649 patients with TNs were collected in a single centre from January 2018 to June 2022, with a total of 273 males and 1376 females, respectively.Measures Seven popular ML models (Random Forest, Decision Tree, Logistic Regression (LR), K-Neighbours, Gaussian Naive Bayes, Multilayer Perception and Gradient Boosting) were developed to predict malignant and benign TNs, whose performance indicators included area under the curve (AUC), accuracy, recall, precision and F1 score.Results A total of 1649 patients were enrolled in this study, with the median age of 45.15±13.41 years, and the male to female ratio was 1:5.055. …”
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Predicting mortality in intensive care unit patients with acute pancreatitis using an interpretable machine learning model by Li Zhuangli, Li Zhuangli, Zhang Xingcheng, Zhang Xiaoli, Lu Zhonghua, Sun Yun

“…After data preprocessing and feature selection via the Least Absolute Shrinkage and Selection Operator (LASSO), seven machine learning models were developed: decision tree, random forest, XGBoost, support vector machine (SVM), multilayer perceptron, k-nearest neighbors (KNN), and logistic regression. …”
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Predicting the risk of postoperative avascular necrosis in patients with talar fractures based on an interpretable machine learning model by Jian Zhang, Jian Zhang, Jian Zhang, Jihai Xu, Jihai Xu, Jiapei Yu, Jiapei Yu, Jiapei Yu, Hong Chen, Hong Chen, Xin Hong, Songou Zhang, Xin Wang, Xin Wang, Chengchun Shen, Chengchun Shen, Chengchun Shen

“…Potential risk factors for postoperative AVN were screened using univariate and multivariate logistic regression analyses. Six machine learning algorithms were employed to construct the prediction models. …”
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High‐Resolution National‐Scale Water Modeling Is Enhanced by Multiscale Differentiable Physics‐Informed Machine Learning by Yalan Song, Tadd Bindas, Chaopeng Shen, Haoyu Ji, Wouter J. M. Knoben, Leo Lonzarich, Martyn P. Clark, Jiangtao Liu, Katie vanWerkhoven, Sam Lamont, Matthew Denno, Ming Pan, Yuan Yang, Jeremy Rapp, Mukesh Kumar, Farshid Rahmani, Cyril Thébault, Richard Adkins, James Halgren, Trupesh Patel, Arpita Patel, Kamlesh Arun Sawadekar, Kathryn Lawson

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Predicting Wind Turbine Blade Tip Deformation With Long Short‐Term Memory (LSTM) Models by Shubham Baisthakur, Breiffni Fitzgerald

“…Using a long short‐term memory (LSTM) model and a novel feature selection approach based on mutual information and recursive feature addition, this study presents a robust framework for multivariate time series prediction. …”
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Evaluating LSTM Performance on Multivariate Time Series with One-Class SVM Outlier Detection by Ragita Anillya Putri, Sugiyarto Surono, Aris Thobirin

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A machine learning model for the computation of thermophysical properties of WCO biodiesel mixed with multiwalled carbon nanotubes by Hussain Syed Sameer, Ali Syed Abbas, Husain Dilawar, Sharma Manish

“…A Machine Learning (ML) model has been developed to compute the thermophysical properties of Waste Cooking Oil (WCO) biodiesel dispersed with MultiWalled Carbon NanoTubes (MWCNTs). …”
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Improve the Intelligent Convenience of Multivariate Optimization of Concrete Mix Ratio and the Development of Corresponding Applications by Zhanfei Yang, Bin Chen, Jianfen Zhou, Saihua Huang

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Non-Intrusive Load Identification Based on Multivariate Features and Information Entropy-Weighted Ensemble by Yue Liu, Wenxia You, Miao Yang

“…Specifically, one-dimensional numerical features related to power and current are input into traditional machine learning models, and two-dimensional image features of binary V-I trajectory are processed by the deep neural network model Swin Transformer. …”
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Investigating factors affecting the quality of water resources by multivariate analysis and soft computing approaches by Bing Cheng, Xinyu Liu, Keke Guo, Ahmad Rastegarnia

“…Support vector machine (SVM) with various kernel functions, multilayer perceptron artificial neural network (MLP-ANN) with various training algorithms, random forest algorithm (RFA), Gaussian process regression (GPR), and statistical analysis methods were used for modeling. …”
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Genome-wide identification and characterization of DNA enhancers with a stacked multivariate fusion framework. by Yansong Wang, Zilong Hou, Yuning Yang, Ka-Chun Wong, Xiangtao Li

“…Here we present a novel, stacked multivariate fusion framework called SMFM, which enables a comprehensive identification and analysis of enhancers from regulatory DNA sequences as well as their interpretation. …”
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Personalized prediction of psoriasis relapse post-biologic discontinuation: a machine learning-driven population cohort study by Shan Huang, Yanping Bai, Ruozhou Qi, Hongda Yu, Xingwu Duan

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Transformer based spatially resolved prediction of mechanical properties in wire arc additive manufacturing by Mohammad Keshmiri, Shirin Dehgahi, Abdullah Mohiuddin, Ahmed J. Qureshi

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Machine learning-based prediction of postoperative pancreatic fistula after laparoscopic pancreaticoduodenectomy by Qianchang Wang, Zhe Wang, Fangfeng Liu, Zhengjian Wang, Qingqiang Ni, Hong Chang

“…Potential risk factors were identified through intergroup comparisons, and independent risk factors were determined using univariate and multivariate logistic regression analyses. Based on these findings, a predictive model for CR-POPF was developed using machine learning algorithms. …”
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Prediction of recurrence-free survival and risk factors of sinonasal inverted papilloma after surgery by machine learning models by Siyu Miao, Yang Cheng, Yaqi Li, Xiaodong Chen, Fuquan Chen, Dingjun Zha, Tao Xue

“…Abstract Objectives Our research aims to construct machine learning prediction models to identify patients proned to recurrence after inverted papilloma (IP) surgery and guide their follow-up treatment. …”
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Spatio-Temporal Predictive Learning Using Crossover Attention for Communications and Networking Applications by Ke He, Thang Xuan Vu, Lisheng Fan, Symeon Chatzinotas, Bjorn Ottersten

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Prediction model of pelvic lymphocysts after cervical cancer surgery based on logistic regression or support vector machine by WANG Jiao, GAO Hui, ZHAO Bo

“…Objective To explore the predictive walue and effect of logistic regression and support vector machine (SVM) model in the formation of pelvic lymphocysts uithin 14 days after cervical cancer surgery. …”
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