Research and application of a novel grey multivariable model in port scale prediction under the impact of Free Trade Zone by Yuyu Sun, Yuchen Zhang, Zhiguo Zhao

“…Practical implications – The new multivariable grey model can effectively reduce the impact of data randomness on forecasting. …”
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Enhanced forecasting of shipboard electrical power demand using multivariate input and variational mode decomposition with mode selection by Paolo Fazzini, Giuseppe La Tona, Matteo Diez, Maria Carmela Di Piazza

“…To address this challenge, this paper introduces a novel hybrid forecasting approach that combines multivariate time series decomposition with Machine Learning (ML) techniques. …”
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Analysis of the most influential factors affecting outcomes of lung transplant recipients: a multivariate prediction model based on UNOS Data by Reza Safdari, Marsa Gholamzadeh, Hamidreza Abtahi, Mehrnaz Asadi Gharabaghi

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Mass medicine vs. personalized medicine: from mathematical methods to regulatory implications by Inderpal S. Randhawa, Grigori Sigalov

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Multivariate predictive modeling of compressive strength in ground granulated blast furnace slag/fly ash-based alkali-activated concrete by Dina A. Emarah

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Why Machine Learning Models Systematically Underestimate Extreme Values by Yuan-Sen Ting

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Exploring Mortality and Prognostic Factors of Heart Failure with In-Hospital and Emergency Patients by Electronic Medical Records: A Machine Learning Approach by Yu CS, Wu JL, Shih CM, Chiu KL, Chen YD, Chang TH

“…To improve the explainability of the AI models, Shapley Additive Explanations methods were also conducted.Conclusion: Exploring HF mortality and its patterns related to clinical risk factors by machine learning models can help physicians make appropriate decisions when monitoring HF patients’ health quality in the hospital.Keywords: mortality, risk factor, cardiovascular disease, multivariate statistical analysis, machine learning, artificial intelligence…”
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Comparing and Optimizing Four Machine Learning Approaches to Radar-Based Quantitative Precipitation Estimation by Miaomiao Liu, Juncheng Zuo, Jianguo Tan, Dongwei Liu

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Optimizing Multivariable Logistic Regression for Identifying Perioperative Risk Factors for Deep Brain Stimulator Explantation: A Pilot Study by Peyton J. Murin, Anagha S. Prabhune, Yuri Chaves Martins

“…Recursive feature elimination with cross-validation (RFECV) optimized factor selection was used. A multivariate logistic regression model was trained and evaluated using precision, recall, F1-score, and area under the curve (AUC). …”
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Integrating environmental clustering to enhance epidemic forecasting with machine learning models by Yosra Didi, Ahlam Walha, Ali Wali

“…This study addresses this gap with a novel forecasting framework that integrates environmental data into predictive modelling. Our key contributions are threefold: (1) we analyse the relationship between environmental variables (temperature, humidity, and air quality) and COVID-19 trends across countries; (2) we propose a two-stage approach combining K-means clustering to group countries based on environmental conditions, followed by region-specific machine learning models using Support Vector Regression (SVR), Prophet, and Long Short-Term Memory (LSTM) networks for both univariate and multivariate time series forecasting; and (3) we demonstrate that LSTM significantly outperforms other models, achieving superior accuracy for 30-day COVID-19 case predictions. …”
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Construction of a prediction model for sarcopenic obesity based on machine learning by Mengru Xu, Mengru Xu, Jia Liu, Jia Liu, Song Hu, Song Hu, Tongxiao Luan, Tongxiao Luan, Yuting Duan, Yuting Duan, Aohua Wang, Aohua Wang, Ziwei Cui, Ziwei Cui, Jing Zhou, Yongjun Mao, Yongjun Mao

“…This study aimed to develop and validate predictive models using machine learning (ML) to identify SO in patients.MethodsData from 386 participants collected at the Affiliated Hospital of Qingdao University were divided into an 8:2 ratio, with 80% used for training and 20% for testing. …”
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Developing a predictive model using multivariate analysis and Long Short-Term Memory (LSTM) to assess corrosion degradation in mining pipeline thickness. by Kalidou Moussa Sow, Nadia Ghazzali

“…To help better detect and prevent it over time, in this paper, we propose a multivariate approach using machine learning. More precisely, we propose to study the evolution of the thickness of the mining pipeline using a multivariate approach and to implement a predictive model using the Long Short-Term Memory (LSTM) artificial neural network. …”
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OCRClassifier: integrating statistical control chart into machine learning framework for better detecting open chromatin regions by Xin Lai, Xin Lai, Min Liu, Yuqian Liu, Xiaoyan Zhu, Jiayin Wang, Jiayin Wang

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A machine learning-based model for predicting survival in patients with Rectosigmoid Cancer. by Yifei Wang, Bingbing Chen, Jinhai Yu

“…Subsequently, models were constructed using six different machine learning algorithms. …”
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Interpretable machine learning models for survival prediction in prostate cancer bone metastases by Hua Zhang, Bingtian Dong, Jialin Han, Lewen Huang

“…However, existing clinical models lack precision. This study seeks to establish machine learning models to improve survival predictions for PCBM patients. …”
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A review of finite control set model predictive control for linear machines by Wei Xu, Han Xiao, Yirong Tang, Jian Ge, Leilei Guo, Abdul Khalique Junejo

“…Among the numerous control methods of LM drive systems, finite control set model predictive control (FCS‐MPC) method has been given special attention due to its clear concept, fast response performance, and ability to handle constrained multivariate non‐linear control problems. …”
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PREDICTIVE MODELS FOR EARLY DETECTION OF PARKINSON’S DISEASE: A MACHINE LEARNING APPROACH by S. Jeyantha Jafna Juliet, D. Jasmine David, J. S. Raj Kumar, Angelin Jeba P., R. Golden Nancy, M. Selvarathi, T. Jemima Jebaseeli

“…These methods involve the analysis of various types of data, including clinical assessments, imaging scans, and genetic markers, to develop accurate predictive models. Even in the initial stages of the conditions, machine learning techniques can discriminate between patients who have and do not have PD by identifying minor variations and traits from such multivariate data. …”
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Construction and validation of immune prognosis model for lung adenocarcinoma based on machine learning by Jinyu Zheng, Xiaoyi Xu, Xianguo Chen, Xianshuai Li, Miao Fu, Yiping Zheng, Jie Yang

“…A multivariate Cox regression model was built to predict survival. …”
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Predictive Modeling for Diabetes Subtype Classification in India: A Machine Learning Approach by Ulagamadesan Venkatesan, Anandakumar Amutha, Ranjit Mohan Anjana, Ranjit Unnikrishnan, Bagavandas Mappillairaju, Viswanathan Mohan

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Rapid differentiation of patients with lung cancers from benign lung nodule based on dried serum Fourier-transform infrared spectroscopy combined with machine learning algorithms by Huanyu Li, Lixue Dai, Shaomei Guo, Hongluan Wang, Lei Lei, Jie Yu, Xiaoyun Li, Jun Wang

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