PLOD3 as a novel oncogene in prognostic and immune infiltration risk model based on multi-machine learning in cervical cancer by Lingling Qiu, Xiuchai Qiu, Xiaoyi Yang

“…We identified 112 key metabolic genes, which were used to construct and validate a prognostic model through various machine learning algorithms. …”
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Learning from the machine: is diabetes in adults predicted by lifestyle variables? A retrospective predictive modelling study of NHANES 2007–2018 by Efrain Riveros Perez, Bibiana Avella-Molano

“…This study is innovative in its integration of machine learning algorithms to predict type 2 diabetes based solely on non-invasive, easily accessible lifestyle and anthropometric variables, demonstrating the potential of data-driven models for early risk assessment without requiring laboratory tests. …”
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The Effect of Family-Centered Empowerment Model Based on Multimedia Education on the Quality of Life of Children Aged 8-12 Years with Thalassemia by Ayoub Mohamadian, Ali Moeini, Mahnaz Sanjari, Zahra Abdullahzade

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Application of iLogic technology in Autodesk inventor to create parametric 3D-model of a gear wheel and conduct research by Petrakova E.A., Samoilova A.S.

“…The article presents an algorithm and tools for creating controlled 3D models using iLogic on the example of a 3D model of a gear wheel. …”
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Developing a logistic regression model to predict spontaneous preterm birth from maternal socio-demographic and obstetric history at initial pregnancy registration by Brenda F. Narice, Mariam Labib, Mengxiao Wang, Victoria Byrne, Joanna Shepherd, Z. Q. Lang, Dilly OC Anumba

“…A three-fold cross-validation technique was applied with subsets for data training and testing in Python® (version 3.8) using the most predictive factors. The model performance was then compared to the previously published predictive algorithms. …”
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Analysis of immune status and prognostic model incorporating lactate metabolism and immune-related genes in clear cell renal cell carcinoma by Jun Wu, Yuqian Wu, Yefeng Sun, Jianhang You, Wenjie Zhang, Tao Zhao

“…The Cox proportional hazards regression model and the LASSO algorithm were combined to screen the core genes related to prognosis. …”
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Utilizing SMOTE-TomekLink and machine learning to construct a predictive model for elderly medical and daily care services demand by Guangmei Yang, Guangdong Wang, Leping Wan, Xinle Wang, Yan He

“…The LightGBM algorithm emerged as the superior prediction model for estimating the service needs of the elderly. …”
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Multiple automated machine-learning prediction models for postoperative reintubation in patients with acute aortic dissection: a multicenter cohort study by Shuyu Wen, Chao Zhang, Junwei Zhang, Ying Zhou, Yin Xu, Minghui Xie, Jinchi Zhang, Zhu Zeng, Long Wu, Weihua Qiao, Xingjian Hu, Xingjian Hu, Nianguo Dong, Nianguo Dong

“…The least absolute shrinkage and selection operator (LASSO) was used for screening risk variables associated with reintubation for subsequent model construction. …”
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An End-to-End Particle Gradation Detection Method for Earth–Rockfill Dams from Images Using an Enhanced YOLOv8-Seg Model by Yu Tang, Shixiang Zhao, Hui Qin, Pan Ming, Tianxing Fang, Jinyuan Zeng

“…A Minimum Area Rectangle algorithm was introduced to compute the gradation, closely matching the results from manual screening. …”
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Derivation and external validation of prediction model for hypertensive disorders of pregnancy in twin pregnancies: a retrospective cohort study in southeastern China by Yuting Gao, Na Lin, Shuisen Zheng, Yujuan Chen, Xiaoling Chen

“…Besides, we included twin pregnancies delivered at Fujian Maternity and Child Health Hospital; Women and Children’s Hospital of Xiamen University from January 2020 to December 2021 as temporal validation set and geographical validation set, respectively.Main outcome measures We performed univariate analysis, the least absolute shrinkage and selection operator regression and Boruta algorithm to screen variables. Then, we used multivariate logistic regression to construct a nomogram that predicted the risk of HDP in twin pregnancies. …”
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Bundled assessment to replace on-road test on driving function in stroke patients: a binary classification model via random forest by Lu Huang, Lu Huang, Xin Liu, Jiang Yi, Yu-Wei Jiao, Tian-Qi Zhang, Guang-Yao Zhu, Shu-Yue Yu, Zhong-Liang Liu, Min Gao, Xiao-Qin Duan

“…The subject was classified as either Success or Unsuccess group according to whether they had completed the on-road test. A random forest algorithm was then applied to construct a binary classification model based on the data obtained from the two groups.ResultsCompared to the Unsuccess group, the Success group had higher scores on the OCS scale for “crossing out the intact heart” (p = 0.015) and lower scores for “executive function” (p = 0.009). …”
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Development and validation of a small-sample machine learning model to predict 5–year overall survival in patients with hepatocellular carcinoma by Tingting Jiang, Xingyu Liu, Wencan He, Hepei Li, Xiang Yan, Qian Yu, Shanjun Mao

“…The SVM algorithm demonstrated superior performance and stability in the internal and external validations of the model. …”
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Hybrid closed-loop systems for managing blood glucose levels in type 1 diabetes: a systematic review and economic modelling by Asra Asgharzadeh, Mubarak Patel, Martin Connock, Sara Damery, Iman Ghosh, Mary Jordan, Karoline Freeman, Anna Brown, Rachel Court, Sharin Baldwin, Fatai Ogunlayi, Chris Stinton, Ewen Cummins, Lena Al-Khudairy

“…Overall survival’s medium-term modelling appeared good, but there was uncertainty about its longer-term modelling. …”
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THE LABORATORY-MODELLING COMPLEX FOR RESEARCH of QUALITY INDICATORS Of TELEVISION TYPE OpTiCAL loСation SYSTEM WORK by R. A. Hutsau, A. S. Solonar, S. V. Tsuprik

“…The structure of a laboratory-modeling complex for researching the quality indicators of algorithms work for detection, measurement, support in optical-location systems is offered, using for this purpose as entrance influence a stream of video of the information of phon and target conditions from the multimedia screen.…”
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Combined use of near infrared spectroscopy and chemometrics for the simultaneous detection of multiple illicit additions in wheat flour by Xinyi Dong, Ying Dong, Jinming Liu, Siting Wu

“…Compared to regression models built with competitive adaptive reweighted sampling and genetic algorithm for feature wavelength selection, the performance improved significantly, enhancing generalization capability. …”
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Predicting the risk of lean non-alcoholic fatty liver disease based on interpretable machine models in a Chinese T2DM population by Shixue Bao, Qiankai Jin, Tieqiao Wang, Yushan Mao, Guoqing Huang

“…Feature screening was performed using the Boruta algorithm and the Least Absolute Shrinkage and Selection Operator (LASSO). …”
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Predicting sleep quality among college students during COVID-19 lockdown using a LASSO-based neural network model by Lufeng Chen, Qingquan Chen, Zhimin Huang, Ling Yao, Jiajing Zhuang, Haibin Lu, Yifu Zeng, Jimin Fan, Ailing Song, Yixiang Zhang

“…A total of eight significant predictors finally identified by the LASSO algorithm was incorporated into prediction models. …”
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Predicting distant metastasis of bladder cancer using multiple machine learning models: a study based on the SEER database with external validation by Xin Chang Zou, Xue Peng Rao, Jian Biao Huang, Jie Zhou, Hai Chao Chao, Tao Zeng

“…Features were filtered using the least absolute shrinkage and selection operator (LASSO) regression algorithm. Based on the significant features identified, three ML algorithms were utilized to develop prediction models: logistic regression, support vector machine (SVM), and linear discriminant analysis (LDA). …”
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A machine learning model revealed that exosome small RNAs may participate in the development of breast cancer through the chemokine signaling pathway by Jun-luan Mo, Xi Li, Lin Lei, Ji Peng, Xiong-shun Liang, Hong-hao Zhou, Zhao-qian Liu, Wen-xu Hong, Ji-ye Yin

“…This study utilized machine learning models to screen for key exosome small RNAs and analyzed and validated them. …”
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Integrative analysis of signaling and metabolic pathways, immune infiltration patterns, and machine learning-based diagnostic model construction in major depressive disorder by Lei Tang, Liling Wu, Mengqin Dai, Nian Liu, Lu liu

“…By comparing the enrichment results across the five datasets, we found that the cell-killing signaling pathway was consistently present in the enriched signaling pathways of all datasets, suggesting that this pathway may play a crucial role in the pathogenesis of MDD. The random forest algorithm (AUC = 0.788) was selected as the optimal algorithm from 113 machine learning algorithms, leading to the development of a robust and predictive MDD algorithm, highlighting the important role of NPL in MDD. …”
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