Development and validation of machine learning models for predicting no. 253 lymph node metastasis in left-sided colorectal cancer using clinical and CT-based radiomic features by Hongwei Zhang, Kexin Wang, Shurong Liu, Guowei Chen, Yong Jiang, Yingchao Wu, Xiaocong Pang, Xiaoying Wang, Junling Zhang, Xin Wang

“…This study aimed to develop a machine learning model for predicting metastasis in No. 253 LN. …”
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Estimation of elbow flexion torque using equilibrium optimizer on feature selection of NMES MMG signals and hyperparameter tuning of random forest regression by Raphael Uwamahoro, Raphael Uwamahoro, Kenneth Sundaraj, Farah Shahnaz Feroz

“…Convergence analysis further revealed that the GLEO algorithm exhibited a superior learning capability compared to EO.ConclusionThis study underscores the potential of the hybrid GLEO approach in selecting highly informative features and optimizing hyperparameters for machine learning models. …”
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Advanced removal of butylparaben from aqueous solutions using magnetic molybdenum disulfide nanocomposite modified with chitosan/beta-cyclodextrin and parametric evaluation through... by Saeed Hosseinpour, Alieh Rezagholizade-shirvan, Mohammad Golaki, Amir Mohammadi, Amir Sheikhmohammadi, Zahra Atafar

“…This research fully evaluates machine learning approaches that optimize complicated environmental remediation operations while advanced nanomaterials used with data-driven optimization provide a strong adaptable method to remove organic water pollutants thus supporting sustainable treatment development. …”
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Predicting aflatoxin contamination in white and yellow maize using Vis/NIR spectroscopy combined with PCA-LDA and PLSR models through aquaphotomics approaches by William Appaw, John-Lewis Zinia Zaukuu, Balkis Aouadi, Eric Tetteh Mensah, Ibok Nsa Oduro, Zoltan Kovacs

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Combination Manner of Sampling Method and Model Structure: The Key Factor for Rice Mapping Based on Sentinel-1 Images Using Data-Driven Machine Learning by Pengliang Wei, Jiao Guo, Jiaqian Lian, Chaoyang Wang

“…Agricultural remote sensing community is increasingly focusing on enhancing crop mapping accuracy by improving data-driven machine-learning model structures, yet ignoring impact of sampling–model structure combination on it, which may prevent full utilization of input data, especially for synthetic aperture radar images with fewer crop prior features. …”
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Structural integrity and hybrid ANFIS-PSO modeling of the corrosion rate of ductile irons in different environments by Ukoba K., Akinribide O.J., Adeleke O.

“…The experimental results from this study were used to validate a model generated from hybrid adaptive neuro-fuzzy inferences system-particle swarm optimization (ANFIS-PSO) algorithms. …”
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Development and validation of a machine learning-based diagnostic model for identifying nonneutropenic invasive pulmonary aspergillosis in suspected patients: a multicenter cohort... by Xinyu Wang, Yajie Lu, Chao Sun, Huanhuan Zhong, Yuchen Cai, Min Cao, Xuefan Cui, Wenkui Sun, Li Wang, Xin Lu, Cheng Chen, Yanbin Chen, Chunlai Feng, Yujian Tao, Jun Zhou, Jiaxin Shi, Guoer Ma, Yuanqin Li, Xin Su

“…ABSTRACT This study aims to develop and validate an optimized diagnostic model for nonneutropenic invasive pulmonary aspergillosis (IPA) among suspected cases. …”
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Utilizing machine learning to predict hospital admissions for pediatric COVID-19 patients (PrepCOVID-Machine) by Chuin-Hen Liew, Song-Quan Ong, David Chun-Ern Ng

“…To curb high hospital admission rates, only patients with genuine medical needs are admitted. However, machine learning (ML) models to predict COVID-19 hospitalization in Asian children are lacking. …”
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Predicting grip strength-related frailty in middle-aged and older Chinese adults using interpretable machine learning models: a prospective cohort study by Lisheng Yu, Lisheng Yu, Shunshun Cao, Botian Song, Yangyang Hu

“…We aimed to explore the association between grip strength and frailty and interpret the optimal machine learning (ML) model using the SHapley Additive exPlanation (SHAP) to predict the risk of frailty.MethodsData for the study were extracted from the China Health and Retirement Longitudinal Study (CHARLS) database. …”
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Evaluating the value of machine learning models for predicting hematoma expansion in acute spontaneous intracerebral hemorrhage based on CT imaging features of hematomas and surrou... by Tianyu Yang, Tianyu Yang, Zhen Zhao, Yan Gu, Shengkai Yang, Yonggang Zhang, Lei Li, Ting Wang, Zhongchang Miao

“…Incorporating the subjective ‘swirl sign’, identified as the most significant feature in univariate analysis, into the simplified model enhanced its performance. This optimized model achieved an AUC of 0.9524, with a sensitivity of 0.9412 and specificity of 0.9091, surpassing both the comprehensive and simplified models.ConclusionThe optimized model, based on CT imaging features of hematomas and surrounding oedema, offers a practical and reliable tool for predicting hematoma expansion in sICH. …”
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The Most Effective Interventions for Classification Model Development to Predict Chat Outcomes Based on the Conversation Content in Online Suicide Prevention Chats: Machine Learnin... by Salim Salmi, Saskia Mérelle, Renske Gilissen, Rob van der Mei, Sandjai Bhulai

“…Using 2 approaches for interpreting machine learning models, we identified text messages from helpers in a chat that contributed the most to the prediction of the model. …”
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Comparative Evaluation of Decision Tree (M5) and Least Square Support Vector Machine (LS-SVM) Models for Groundwater Level Prediction in the Mashhad Plain by Vajihe Ramezani saani, Mahdi Zarei, Seyed Morteza Seyedian

“…A comparison of the results of the models indicated that the LS-SVM model is more sensitive to changes in input parameters than the M5 model, such that the decision tree model, unlike the least squares support vector machine model, provided acceptable results in all scenarios.Conclusions: In summary, the comparison of the models used suggests that the appropriate selection of climatic parameters and the examination and analysis of data have a significant impact on the accuracy of predictions.…”
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Comparative performance of twelve machine learning models in predicting COVID-19 mortality risk in children: a population-based retrospective cohort study in Brazil by Adriano Lages dos Santos, Maria Christina L. Oliveira, Enrico A. Colosimo, Robert H. Mak, Clara C. Pinhati, Stella C. Gallante, Hercílio Martelli-Júnior, Ana Cristina Simões e Silva, Eduardo A. Oliveira

“…However, the development of machine learning (ML) models for predicting outcomes in children and adolescents with COVID-19 remains limited. …”
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A machine learning model integrating clinical-radiomics-deep learning features accurately predicts postoperative recurrence and metastasis of primary gastrointestinal stromal tumor... by WenJie Xie, Zhen Zhang, Zhao Sun, XiaoChen Wan, JieHan Li, JianWu Jiang, Qi Liu, Ge Yang, Yang Fu

“…The optimal clinical application scenarios of the model were further explored by comparing the DCA performance of the two subgroups. …”
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Intelligent predictive risk assessment and management of sarcopenia in chronic disease patients using machine learning and a web-based tool by Ke Rong, Gu li jiang Yi ke ran, Changgui Zhou, Xinglin Yi

“…A generalized linear mixed model (GLMM) with random effects and diverse machine learning models were utilized to explore feature contributions to sarcopenia risk. …”
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Predicting Pathological Complete Response Following Neoadjuvant Therapy in Patients With Breast Cancer: Development of Machine Learning–Based Prediction Models in a Retrospective S... by Chun-Chi Lai, Cheng-Yu Chen, Tzu-Hao Chang

“…ObjectiveThe objective of this study was to develop robust, machine learning–based prediction models for pCR following neoadjuvant therapy, leveraging clinical, laboratory, and imaging data. …”
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Interpretable multiparametric MRI radiomics-based machine learning model for preoperative differentiation between benign and malignant prostate masses: a diagnostic, multicenter st... by Wenjun Zhou, Wenjun Zhou, Zhangcheng Liu, Zhangcheng Liu, Jindong Zhang, Shuai Su, Yu Luo, Lincen Jiang, Kun Han, Guohua Huang, Jue Wang, Jianhua Lan, Delin Wang

“…ObjectiveThe study aimed to develop and externally validate multiparametric MRI (mpMRI) radiomics-based interpretable machine learning (ML) model for preoperative differentiating between benign and malignant prostate masses.MethodsPatients who underwent mpMRI with suspected malignant prostate masses were retrospectively recruited from two independent hospitals between May 2016 and May 2023. …”
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Prediction of carbon dioxide emissions from Atlantic Canadian potato fields using advanced hybridized machine learning algorithms – Nexus of field data and modelling by Muhammad Hassan, Khabat Khosravi, Aitazaz A. Farooque, Travis J. Esau, Alaba Boluwade, Rehan Sadiq

“…In this study, three novel machine learning algorithms of additive regression-random forest (AR-RF), Iterative Classifier Optimizer (ICO-AR-RF), and multi-scheme (MS-RF) were explored for carbon dioxide (CO2) flux rate prediction from three agricultural fields. …”
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Developing a Machine Learning Model for Predicting 30-Day Major Adverse Cardiac and Cerebrovascular Events in Patients Undergoing Noncardiac Surgery: Retrospective Study by Ju-Seung Kwun, Houng-Beom Ahn, Si-Hyuck Kang, Sooyoung Yoo, Seok Kim, Wongeun Song, Junho Hyun, Ji Seon Oh, Gakyoung Baek, Jung-Won Suh

“…ResultsAll machine learning prediction models surpassed the Revised Cardiac Risk Index in MACCE prediction performance (AUROC=0.704). …”
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Multi-cohort study in gastric cancer to develop CT-based radiomic models to predict pathological response to neoadjuvant immunotherapy by Ze-Ning Huang, Hao-Xiang Zhang, Yu-Qin Sun, Xing-Qi Zhang, Yi-Fen Lin, Cai-Ming Weng, Chao-Hui Zheng, Ping-Li, Jia-Bin Wang, Qi-Yue Chen, Long-Long Cao, Mi Lin, Ru-Hong Tu, Chang-Ming Huang, Jian-Xian Lin, Jian-Wei Xie

“…Nine ML algorithms were implemented to build prediction models, with the optimal algorithm selected for the final prediction model. …”
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