Development and validation of a quick screening tool for predicting neck pain patients benefiting from spinal manipulation: a machine learning study by Changxiao Han, Guangyi Yang, Haibao Wen, Minrui Fu, Bochen Peng, Bo Xu, Xunlu Yin, Ping Wang, Liguo Zhu, Minshan Feng

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Predicting the generalization of computer aided detection (CADe) models for colonoscopy by Joel Shor, Carson McNeil, Yotam Intrator, Joseph R. Ledsam, Hiro-o Yamano, Daisuke Tsurumaru, Hiroki Kayama, Atsushi Hamabe, Koji Ando, Mitsuhiko Ota, Haruei Ogino, Hiroshi Nakase, Kaho Kobayashi, Masaaki Miyo, Eiji Oki, Ichiro Takemasa, Ehud Rivlin, Roman Goldenberg

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Automatic segmentation model and machine learning model grounded in ultrasound radiomics for distinguishing between low malignant risk and intermediate-high malignant risk of adnex... by Lu Liu, Wenjun Cai, Feibo Zheng, Hongyan Tian, Yanping Li, Ting Wang, Xiaonan Chen, Wenjing Zhu

“…Critical relevance statement The ultrasound radiomics-based machine learning model holds the potential to elevate the professional ability of less-experienced radiologists and can be used to assist in the clinical screening of ovarian cancer. …”
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Applications of AI-Based Models for Online Fraud Detection and Analysis by Antonis Papasavva, Samantha Lundrigan, Ed Lowther, Shane Johnson, Enrico Mariconti, Anna Markovska, Nilufer Tuptuk

“…Results We discuss the state-of-the-art AI and NLP techniques used to analyse various online fraud categories; the data sources used for training the AI and NLP models; the AI and NLP algorithms and models built; and the performance metrics employed for model evaluation. …”
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Predictive performance of risk prediction models for lung cancer incidence in Western and Asian countries: a systematic review and meta-analysis by Yah Ru Juang, Lina Ang, Wei Jie Seow

“…Abstract Numerous prediction models have been developed to identify high-risk individuals for lung cancer screening, with the aim of improving early detection and survival rates. …”
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A machine learning-based screening model for the early detection of prostate cancer developed using serum microRNA data from a mixed cohort of 8,741 participants by Cong Lai, Zhensheng Hu, Zhuohang Li, Zhikai Wu, Kuiqing Li, Lin Li, Hongze Liu, Juanyi Shi, Yi Zhou, Kewei Xu, Cheng Liu

“…Six machine learning algorithms were employed to develop a screening model for PCa using the training dataset. …”
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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

“…Abstract Tremendous progress has been made to control the COVID‐19 pandemic caused by the SARS‐CoV‐2 virus. …”
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Comparing the performance of screening surveys versus predictive models in identifying patients in need of health-related social need services in the emergency department. by Olena Mazurenko, Adam T Hirsh, Christopher A Harle, Joanna Shen, Cassidy McNamee, Joshua R Vest

“…We built an XGBoost classification algorithm using responses from the screening questionnaire to predict HRSN needs (screening questionnaire model). …”
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Machine Learning‐Based High‐Throughput Screening, Molecular Modeling and Quantum Chemical Analysis to Investigate Mycobacterium tuberculosis MetRS Inhibitors by Rajesh Maharjan, Kalpana Gyawali, Arjun Acharya, Madan Khanal, Kamal Khanal, Mohan Bahadur Kshetri, Dr. Madhav Prasad Ghimire, Dr. Tika Ram Lamichhane

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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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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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Prediction and Screening of Lead-Free Double Perovskite Photovoltaic Materials Based on Machine Learning by Juan Wang, Yizhe Wang, Xiaoqin Liu, Xinzhong Wang

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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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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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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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Model of a Novel PCB Coil for High-Sensitivity Metal Detector by Han Zhang, Mingxing Song, Yuejiu Zhu, Xianze Xu, Fengqiu Xu

“…An optimization problem is constructed from the numerical model, and the optimal design parameters of the receiving coil are determined via a heuristic algorithm. …”
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Study on the Impact of Input Parameters on Seawater Dissolved Oxygen Prediction Models by Wenqing Li, Jing Lv, Yuhang Wang, Xiangfeng Kong

“…Future research will develop a parameter adaptive selection algorithm, conduct the dynamic monitoring of multi-scale environmental factors, and achieve the intelligent optimization and verification of model parameters.…”
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A diagnostic model for polycystic ovary syndrome based on machine learning by Cheng Tong, Yue Wu, Zhenchao Zhuang, Ying Yu

“…The data of 10 case groups and 10 control groups were randomly selected as validation set data, and the rest of the data were included in the model construction. The acquired data were screened for variables, a classification model based on a machine learning algorithm was constructed, and the constructed model was evaluated and validated for diagnostic efficacy. …”
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