Predictive model establishment for forward-head posture disorder in primary-school-aged children based on multiple machine learning algorithms by Hongjun Tao, Yang Wen, Rongfang Yu, Yining Xu, Fangliang Yu

“…Multiple machine learning algorithms are applied to construct distinct risk prediction models, with the most effective model selected through comparative analysis. …”
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Development and validation of a risk prediction model for kinesiophobia in postoperative lung cancer patients: an interpretable machine learning algorithm study by Chuang Li, Youbei Lin, Xuyang Xiao, Xinru Guo, Jinrui Fei, Yanyan Lu, Junling Zhao, Lan Zhang

“…This study demonstrates that machine learning models—particularly the RF algorithm—hold substantial promise for predicting kinesiophobia in postoperative lung cancer patients. …”
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Research of color models in digital graphics by Hetman Oksana, Shpetna Svitlana

“…The study focuses on a detailed examination of the RGB, CMYK, HSL/HSV, and LAB color models. It is established that the RGB model is an additive system optimized for screens and displays, as it provides a broad and vibrant color range suitable for digital applications. …”
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A diagnostic prediction model for anti-neutrophil cytoplasmic antibody associated vasculitis combined with glomerulonephritis based on machine learning algorithm by Wang Jian-mei, Zhu Ge-li, Cao Chen-lin, Peng Qing-quan

“…<italic>EHHADH</italic>， <italic>CCL2</italic>， <italic>FN1</italic>， <italic>IL1B</italic>， <italic>VAV1</italic>， <italic>CXCR4</italic>， <italic>CCL5</italic>， and <italic>CD44</italic>were core genes in the PPI network. The RF algorithm screened out 15 characteristic genes， and the artificial neural network algorithm calculated the weight of each characteristic gene and successfully constructed a diagnostic model. …”
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A diagnostic prediction model for anti-neutrophil cytoplasmic antibody associated vasculitis combined with glomerulonephritis based on machine learning algorithm by Wang Jian-mei, Zhu Ge-li, Cao Chen-lin, Peng Qing-quan

“…EHHADH， CCL2， FN1， IL1B， VAV1， CXCR4， CCL5， and CD44were core genes in the PPI network. The RF algorithm screened out 15 characteristic genes， and the artificial neural network algorithm calculated the weight of each characteristic gene and successfully constructed a diagnostic model. …”
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Development of machine learning models to predict the risk of fungal infection following flexible ureteroscopy lithotripsy by Haofang Zhang, Changbao Xu, Chenge Hu, Yunlai Xue, Daoke Yao, Yifan Hu, Ankang Wu, Miao Dai, Hang Ye

“…Our study aimed to construct a machine learning algorithm predictive model to predict the risk of fungal infection following F-URL. …”
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Machine learning algorithms as early diagnostic tools for prolonged operative time in patients with fluorescent laparoscopic cholecystectomy: a retrospective cohort study by Chu Wang, Chu Wang, JunYe Wen, ZiYi Su, HanXiang Yu

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Identification of maize kernel varieties based on interpretable ensemble algorithms by Chunguang Bi, Chunguang Bi, Xinhua Bi, Jinjing Liu, Hao Xie, Shuo Zhang, He Chen, Mohan Wang, Lei Shi, Lei Shi, Shaozhong Song

“…Morphological and hyperspectral data of maize samples were extracted and preprocessed, and three methods were used to screen features, respectively. The base learner of the Stacking integration model was selected using diversity and performance indices, with parameters optimized through a differential evolution algorithm incorporating multiple mutation strategies and dynamic adjustment of mutation factors and recombination rates. …”
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Advancing Alzheimer’s disease risk prediction: development and validation of a machine learning-based preclinical screening model in a cross-sectional study by Yanfei Chen, Bing Wang, Yankai Shi, Wenhao Qi, Shihua Cao, Bingsheng Wang, Ruihan Xie, Jiani Yao, Xiajing Lou, Chaoqun Dong, Xiaohong Zhu, Danni He

“…The study utilised Random Forest and Extreme Gradient Boosting (XGBoost) algorithms alongside traditional logistic regression for modelling. …”
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i-DENV: development of QSAR based regression models for predicting inhibitors targeting non-structural (NS) proteins of dengue virus by Sakshi Gautam, Anamika Thakur, Manoj Kumar

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Genome‐scale metabolic modeling reveals SARS‐CoV‐2‐induced metabolic changes and antiviral targets by Kuoyuan Cheng, Laura Martin‐Sancho, Lipika R Pal, Yuan Pu, Laura Riva, Xin Yin, Sanju Sinha, Nishanth Ulhas Nair, Sumit K Chanda, Eytan Ruppin

“…We next applied the GEM‐based metabolic transformation algorithm to predict anti‐SARS‐CoV‐2 targets that counteract the virus‐induced metabolic changes. …”
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Unlocking The Potential of Hybrid Models for Prognostic Biomarker Discovery in Oral Cancer Survival Analysis: A Retrospective Cohort Study by Leila Nezamabadi Farahani, Anoshirvan Kazemnejad, Mahlagha Afrasiabi, Leili Tapak

“…Concordance index (C-index), mean absolute error (MAE), mean squared error (MSE) and R-squares, were used to evaluate the performance of the models using selected features. Functional enrichment analysis was performed using DAVID database, and external validation utilized three independent datasets (GSE9844, GSE75538, GSE37991, GSE42743).Results: The findings indicated that the PSO-based method outperformed the GA-based method, achieving a smaller MAE (0.061) and MSE (0.005), R-square (0.99) and C-index (0.973), selecting 291 probes from 1069 screened. …”
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Fibrosis-4plus score: a novel machine learning-based tool for screening high-risk varices in compensated cirrhosis (CHESS2004): an international multicenter study by Bingtian Dong, Ruiling He, Shenghong Ju, Yuping Chen, Ivica Grgurevic, Jianzhong Ma, Ying Guo, Huizhen Fan, Qiang Yan, Chuan Liu, Huixiong Xu, Anita Madir, Kristian Podrug, Jia Wang, Linxue Qian, Zhengzi Geng, Shanghao Liu, Tao Ren, Guo Zhang, Kun Wang, Meiqin Su, Fei Chen, Sumei Ma, Liting Zhang, Zhaowei Tong, Yonghe Zhou, Xin Li, Fanbin He, Hui Huan, Wenjuan Wang, Yunxiao Liang, Juan Tang, Fang Ai, Tingyu Wang, Liyun Zheng, Zhongwei Zhao, Jiansong Ji, Wei Liu, Jiaojiao Xu, Bo Liu, Xuemei Wang, Yao Zhang, Qiong Yan, Hui Liu, Xiaomei Chen, Shuhua Zhang, Yihua Wang, Yang Liu, Li Yin, Yanni Liu, Yanqing Huang, Li Bian, Ping An, Xin Zhang, Shaoting Zhang, Jinhua Shao, Xiangman Zhang, Wei Rao, Chaoxue Zhang, Christoph Frank Dietrich, Won Kim, Xiaolong Qi

“…Shapley Additive exPlanations method was used to interpret the model predictions. Results We analyzed data from 502 patients with compensated cirrhosis who underwent EGD screening. …”
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Experimental investigations and field applications of a tension estimation method for two linked suspenders using only local vibration measurements by Wen-Hwa Wu, Chien-Chou Chen, Meng-Xuan Wu, Gwolong Lai

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Development of prediction models for screening depression and anxiety using smartphone and wearable-based digital phenotyping: protocol for the Smartphone and Wearable Assessment f... by Sujin Kim, Ah Young Kim, Yu-Bin Shin, Seonmin Kim, Min-Sup Shin, Jinhwa Choi, Kyung Lyun Lee, Jisu Lee, Sangwon Byun, Heon-Jeong Lee, Chul-Hyun Cho

“…The Smartphone and Wearable Assessment for Real-Time Screening of Depression and Anxiety study aims to develop prediction algorithms to identify individuals at risk for depressive and anxiety disorders, as well as those with mild-to-severe levels of either condition or both. …”
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Machine Learning Model for Early Detection of COVID-19 by Heart Rhythm Abnormalities by M. S. Mezhov, V. O. Kozitsin, Iu. D. Katser

“…The work aims at creating a mathematical model based on machine learning algorithms to automate the process of detecting covid abnormalities in the heart rhythm. …”
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COMPUTER- AIDED MODELING AND IMPROVING OF RISOGRAPH PRINTING by P. E. Sulim, V. S. Yudenkov

“…The considered improvement of qualit y of the risofraph print based on a mathematical model in the environment Matlab by using the specialized algorithms and digital filter of the Image Processing Toolbox. …”
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Screening risk factors for the occurrence of wedge effects in intramedullary nail fixation for intertrochanteric fractures in older people via machine learning and constructing a p... by Zhe Xu, Qiuhan Chen, Zhi Zhou, Jianbo Sun, Guang Tian, Chen Liu, Guangzhi Hou, Ruguo Zhang

“…The purpose of this study was to screen risk factors for the intraoperative V-effect in intertrochanteric fractures and to develop a clinical prediction model. …”
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Screening Model for Bladder Cancer Early Detection With Serum miRNAs Based on Machine Learning: A Mixed‐Cohort Study Based on 16,189 Participants by Cong Lai, Zhensheng Hu, Jintao Hu, Zhuohang Li, Lin Li, Mimi Liu, Zhikai Wu, Yi Zhou, Cheng Liu, Kewei Xu

“…Five machine learning algorithms were utilized to develop screening models for BCa using the training dataset. …”
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Screening for More than 1,000 Pesticides and Environmental Contaminants in Cannabis by GC/Q-TOF by Philip L. Wylie, Jessica Westland, Mei Wang, Mohamed M. Radwan, Chandrani G. Majumdar, Mahmoud A. ElSohly

“… A method has been developed to screen cannabis extracts for more than 1,000 pesticides and environmental pollutants using a gas chromatograph coupled to a high-resolution accurate mass quadrupole time-of-flight mass spectrometer (GC/Q-TOF). …”
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