Evaluating Machine Learning and Deep Learning models for predicting Wind Turbine power output from environmental factors. by Montaser Abdelsattar, Mohamed A Ismeil, Karim Menoufi, Ahmed AbdelMoety, Ahmed Emad-Eldeen

“…This means that they are better at modeling non-linear dependencies in multivariate data. …”
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Noninvasive diagnosis of significant liver fibrosis in patients with chronic hepatitis B using nomogram and machine learning models by Chuan Jiang, Zhenyu Xu, Jinqing Liu, Ronghua Li, Keyu Chen, Wenting Peng, Yueming Xiao, Da Cheng, Lei Fu, Shifang Peng

“…The nomogram was developed based on univariate analysis and multivariate regression analysis. Various machine learning models were employed to construct prediction models for significant liver fibrosis. …”
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Construction of machine learning-based prognostic model of centrosome amplification-related genes for esophageal squamous cell carcinoma by LI Chaoqun, ZHENG Hongliang, HUANG Ping

“…Subsequently, single-sample gene set enrichment analysis (ssGSEA) and weighted gene co-expression network analysis (WGCNA) were employed to screen CARGs. A prognostic model of CARGs was constructed by incorporating 12 machine learning algorithms, and univariate and multivariate Cox regression analyses were applied to evaluate whether the 12 models as an independent prognostic factor or not. …”
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XGBoost model for predicting erectile dysfunction risk after radical prostatectomy: development and validation using machine learning by Hesong Jiang, Lu Ji, Leilei Zhu, Hengbing Wang, Fei Mao

“…This study aims to develop a machine learning-based model to improve ED risk stratification and guide personalized management. …”
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Early detection of positive urine culture in patients with urolithiasis: a machine learning model with dynamic online nomogram by Di Luo, Jingdong Zhang, Linguo Xie, Chunyu Liu, Rui Wang

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Development and validation of machine learning models for distant metastasis of primary hepatic carcinoma: a population-based study by Cong Lu, Ying He, Chun-Ru Chen, Lun Wu, Dan Song, Chen-Hong Wang, Le-Qing Zhang, Jing-Yi Miao, Yong-Bin Zheng, Wei Wang

“…Eight machine learning models were constructed using the “tidymodels” package in R and evaluated based on ROC curves, AUC, and accuracy. …”
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Development of a Prognostic Nomogram for Nonneutropenic Invasive Pulmonary Aspergillosis Based on Machine Learning by Du W, Ji W, Luo T, Zhang Y, Guo W, Liang J, Lv Y

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A Multi-Algorithm Machine Learning Model for Predicting the Risk of Preterm Birth in Patients with Early-Onset Preeclampsia by Xu Y, Zu Y, Zhang Y, Liang Z, Xu X, Yan J

“…Eight machine learning models were trained (70% data) and validated (30% data). …”
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Predictive model for sarcopenia in chronic kidney disease: a nomogram and machine learning approach using CHARLS data by Renjie Lu, Shiyun Wang, Pinghua Chen, Fangfang Li, Fangfang Li, Pan Li, Qian Chen, Xuefei Li, Fangyu Li, Suxia Guo, Jinlin Zhang, Jinlin Zhang, Dan Liu, Zhijun Hu

“…ROC and DCA analyses confirmed the model’s strong predictive performance. The Gradient Boosting Machine (GBM) outperformed other machine learning models. …”
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Protocol for a systematic review on the methodological and reporting quality of prediction model studies using machine learning techniques by Gary S Collins, Richard D Riley, Johanna A A G Damen, Lotty Hooft, Ram Bajpai, Jie Ma, Constanza L Andaur Navarro, Toshihiko Takada, Steven W J Nijman, Paula Dhiman, Karel GM Moons

“…Due to the increasing availability of larger, routinely collected and complex medical data, and the rising application of Artificial Intelligence (AI) or machine learning (ML) techniques, the number of prediction model studies is expected to increase even further. …”
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Reporting and risk of bias of prediction models based on machine learning methods in preterm birth: A systematic review by Qiuyu Yang, Xia Fan, Xiao Cao, Weijie Hao, Jiale Lu, Jia Wei, Jinhui Tian, Min Yin, Long Ge

“…Abstract Introduction There was limited evidence on the quality of reporting and methodological quality of prediction models using machine learning methods in preterm birth. …”
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Network-based predictive models for artificial intelligence: an interpretable application of machine learning techniques in the assessment of depression in stroke patients by Wenwei Zuo, Xuelian Yang

“…First, risk factors for depression in stroke patients were determined by univariate and multivariate logistic regression analysis. Next, five machine learning algorithms were used to construct predictive models, and several evaluation metrics (including area under the curve (AUC)) were used to compare the predictive performance of the models. …”
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Explainable machine learning-driven models for predicting Parkinson’s disease and its prognosis: obesity patterns associations and models development using NHANES 1999–2018 data... by Jiaxin Fan, Shuai Cao, Hang Peng, Yuanjie Zhi, Shuqin Zhan, Rui Li

“…We aimed to investigate the associations of obesity patterns on PD and all-cause mortality, while developing machine learning (ML)-driven predictive and prognostic models for PD. …”
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Identification of peanut oil origins based on Raman spectroscopy combined with multivariate data analysis methods by Peng-fei ZHU, Qing-li YANG, Hai-yan ZHAO

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Multimodal data integration with machine learning for predicting PARP inhibitor efficacy and prognosis in ovarian cancer by Xi’an Xiong, Li Cai, Li Cai, Zhen Yang, Zhongping Cao, Nayiyuan Wu, Nayiyuan Wu, Qianxi Ni

“…Patient-specific efficacy and prognosis prediction models were then constructed using various machine learning algorithms.ResultsTotal bile acids (TBAs) and CA-199 present as an independent risk factor in Cox multivariate analysis for primary and recurrent ovarian cancer patients respectively (P < 0.05). …”
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Pedotransfer functions for estimating the van Genuchten model parameters in the Cerrado biome by Mariana F. Veloso, Lineu N. Rodrigues, Elpídio I. Fernandes Filho, Carolina F. Veloso, Bruna N. Rezende

“…The current study aimed to develop and evaluate PTFs to estimate the fit parameters of the van Genuchten model for the Cerrado biome. Multiple Linear Regression (MLR) and four machine learning (ML) algorithms were used to develop the PTFs. …”
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Research on dust identification and concentration detection method based on machine vision by Luyang TU, Qinghua CHEN, Yingsong CHENG, Bingyou JIANG

“…Aiming at the problem that the current machine vision algorithm fails to combine position information with concentration value in the field of dust detection, we propose an algorithm that combines improved YOLOv5 with multivariate model. …”
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Predicting apheresis yield and factors affecting peripheral blood stem cell harvesting using a machine learning model by Jing Qi, Yinchu Chen, Xiaoke Jin, Ran Wang, Nana Wang, Jiawei Yan, Chen Huang, Jun Huang, Yuanfeng Wei, Faqin Xie, Zhengzhi Yu, Dongping Huang

“…The multivariate logistic regression results were integrated into various machine learning models to assess predictive accuracy. …”
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A comprehensive machine learning-based models for predicting mixture toxicity of azole fungicides toward algae (Auxenochlorella pyrenoidosa) by Li-Tang Qin, Xue-Fang Tian, Jun-Yao Zhang, Yan-Peng Liang, Hong-Hu Zeng, Ling-Yun Mo

“…In this study, we applied 12 algorithms, namely, k-nearest neighbor (KNN), kernel k-nearest neighbors (KKNN), support vector machine (SVM), random forest (RF), stochastic gradient boosting (GBM), cubist, bagged multivariate adaptive regression splines (Bagged MARS), eXtreme gradient boosting (XGBoost), boosted generalized linear model (GLMBoost), boosted generalized additive model (GAMBoost), bayesian regularized neural networks (BRNN), and recursive partitioning and regression trees (CART) to build ML models for 225 mixture toxicity of azole fungicides towards Auxenochlorella pyrenoidosa. …”
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Machine learning-based diagnostic and prognostic models for breast cancer: a new frontier on the clinical application of natural killer cell-related gene signatures in precision me... by Yutong Fang, Rongji Zheng, Yefeng Xiao, Qunchen Zhang, Junpeng Liu, Jundong Wu

“…Differentially expressed genes (DEGs) were identified, and key prognostic NRGs were selected using univariate and multivariate Cox regression analyses. We constructed ML-based diagnostic models using 12 algorithms and evaluated their performance for identifying the optimal ML diagnostic model. …”
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