A comprehensive and bias-free machine learning approach for risk prediction of preeclampsia with severe features in a nulliparous study cohort by Yun C. Lin, Daniel Mallia, Andrea O. Clark-Sevilla, Adam Catto, Alisa Leshchenko, Qi Yan, David M. Haas, Ronald Wapner, Itsik Pe’er, Anita Raja, Ansaf Salleb-Aouissi

“…However, since our model includes various factors that exhibit a positive correlation with PLGF, such as blood pressure measurements and BMI, we have employed an algorithmic approach to disentangle this bias from the model. …”
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Integrated single-cell and bulk RNA dequencing to identify and validate prognostic genes related to T Cell senescence in acute myeloid leukemia by Mengyao Sha, Jun Chen, Haifeng Hou, Huaihui Dou, Yan Zhang

“…Univariate and multivariate regression analyses were performed to screen prognostic genes using the AML Cohort in The Cancer Genome Atlas (TCGA) Database (TCGA-LAML), and risk models were constructed to identify high-risk and low-risk patients. …”
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XGBoost Algorithm for Cervical Cancer Risk Prediction: Multi-dimensional Feature Analysis by Sudi Suryadi, Masrizal

“…This study is situated at the intersection of clinical oncology and computational intelligence, exploring the potential of gradient-boosting algorithms to overcome the limitations of conventional screening methodologies. …”
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Forest Aboveground Biomass Estimation Based on Unmanned Aerial Vehicle–Light Detection and Ranging and Machine Learning by Yan Yan, Jingjing Lei, Yuqing Huang

“…In this study, the performance of predictive biomass regression equations and machine learning algorithms, including multivariate linear stepwise regression (MLSR), support vector machine regression (SVR), and k-nearest neighbor (KNN) for constructing a predictive forest AGB model was analyzed and compared at individual tree and stand scales based on forest parameters extracted by Unmanned Aerial Vehicle–Light Detection and Ranging (UAV LiDAR) and variables screened by variable projection importance analysis to select the best prediction method. …”
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Construction of a novel radioresistance-related signature for prediction of prognosis, immune microenvironment and anti-tumour drug sensitivity in non-small cell lung cancer by Yanliang Chen, Chan Zhou, Xiaoqiao Zhang, Min Chen, Meifang Wang, Lisha Zhang, Yanhui Chen, Litao Huang, Junjun Sun, Dandan Wang, Yong Chen

“…The least absolute shrinkage and selection operator (LASSO) regression and random survival forest (RSF) were used to screen for prognostically relevant RRRGs. Multivariate Cox regression was used to construct a risk score model. …”
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Translational medicine research on the role of key gene network modulation mediated by procyanidin B2 in the precise diagnosis and treatment of multiple sclerosis by Jian Liu, Meng Pu, Di Guo, Ying Xiao, Jin-zhu Yin, Dong Ma, Cun-gen Ma, Qing Wang

“…Eight machine learning algorithms were employed to screen key genes, and nomograms and ROC curves were constructed to assess the value of the screened biomarker genes in MS diagnosis. …”
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Cost-effectiveness analysis of MASLD screening using FIB-4 based two-step algorithm in the medical check-up by Mimi Kim, Huiyul Park, Eileen L. Yoon, Ramsey Cheung, Donghee Kim, Hye-Lin Kim, Dae Won Jun

“…We constructed a hybrid model of the decision tree model and Markov model to compare expected costs and quality-adjusted life-years (QALYs) between ‘screening’ and ‘no screening’ groups from healthcare system perspectives. …”
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A large-scale prospective nested case-control study: developing a comprehensive risk prediction model for early detection of pancreatic cancer in the community-based ESPRIT-AI coho... by Chaoliang Zhong, Penghao Li, Jia Zhao, Xue Han, Beilei Wang, Gang Jin

“…Multiple machine learning algorithms were compared, with the best performing algorithm selected for the final predictive model, subsequently validated using a real-world external test cohort. …”
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Predicting onset of myopic refractive error in children using machine learning on routine pediatric eye examinations only by Yonina Ron, Tchelet Ron, Naomi Fridman, Anat Goldstein

“…This study develops machine learning (ML) models to predict future myopia development. These models utilize easily accessible, non-invasive data gathered during standard eye clinic visits, deliberately excluding more complex measurements such as axial length or corneal curvature. …”
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Identification and validation of aging related genes in osteoarthritis by Jian Du, Jian Du, Tian Zhou, Yanghui Dong, Yunchao Sun, Wei Peng

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Predicting Superaverage Length of Stay in COPD Patients with Hypercapnic Respiratory Failure Using Machine Learning by Zuo B, Jin L, Sun Z, Hu H, Yin Y, Yang S, Liu Z

“…Ten machine learning algorithms were used to develop and validate a model for predicting superaverage length of stay, and the best model was evaluated and selected.Results: We screened 83 candidate variables using the Boruta algorithm and identified 9 potentially important variables, including: cerebrovascular disease, white blood cell count, hematocrit, D-dimer, activated partial thromboplastin time, fibrin degradation products, partial pressure of carbon dioxide, reduced hemoglobin, and oxyhemoglobin. …”
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Automated Audit and Self-Correction Algorithm for Seg-Hallucination Using MeshCNN-Based On-Demand Generative AI by Sihwan Kim, Changmin Park, Gwanghyeon Jeon, Seohee Kim, Jong Hyo Kim

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Exploratory Study on Screening Chronic Renal Failure Based on Fourier Transform Infrared Spectroscopy and a Support Vector Machine Algorithm by Yushuai Yuan, Li Yang, Rui Gao, Cheng Chen, Min Li, Jun Tang, Xiaoyi Lv, Ziwei Yan

“…The samples were input into the SVM after division by the Kennard–Stone (KS) algorithm. Compared with other models, the SVM optimized by a grid search (GS) algorithm performed the best. …”
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Empowering Portable Age-Related Macular Degeneration Screening: Evaluation of a Deep Learning Algorithm for a Smartphone Fundus Camera by Ashish Sharma, Taraprasad Das, Bryan Ong, Florian Mickael Savoy, Divya Parthasarathy Rao, Jun Kai Toh, Anand Sivaraman

“…The model was designed to indicate the presence of referable AMD (intermediate and advanced AMD). …”
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To Determine the Risk-Based Screening Interval for Diabetic Retinopathy: Development and Validation of Risk Algorithm from a Retrospective Cohort Study by Jinxiao Lian, Ching So, Sarah Morag McGhee, Thuan-quoc Thach, Cindy Lo Kuen Lam, Colman Siu Cheung Fung, Alfred Siu Kei Kwong, Jonathan Cheuk Hung Chan

“…Methods The retrospective cohort data from 117,418 subjects who received systematic DR screening in Hong Kong between 2010 and 2016 were included to develop and validate the risk algorithm using a parametric survival model. …”
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Recent Applications of In Silico Approaches for Studying Receptor Mutations Associated with Human Pathologies by Matteo Pappalardo, Federica Maria Sipala, Milena Cristina Nicolosi, Salvatore Guccione, Simone Ronsisvalle

“…The reported techniques include virtual screening, homology modeling, threading, docking, and molecular dynamics. …”
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Driver injury severity in two-vehicle accidents considering collision role by JIN Wenzhou, PEI Xiaohang, TANG Zuogan, YAO Yinjie

“…In order to study the influencing factors of driver injury severity and the interaction effects of collision roles in two-vehicle accidents, based on the data of two-vehicle collision accidents in Shenzhen from 2018 to 2020, we calculate the value of importance degree of characteristic variables by using random forest algorithm to screen out the candidate independent variables, and establish a binary logit model of driver injury severity considering collision angle. …”
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Suicide Risk Screening in Jails: Protocol for a Pilot Study Leveraging the Mental Health Research Network Algorithm and Health Care Data by Erin B Comartin, Grant Victor, Athena Kheibari, Brian K Ahmedani, Bethany Hedden-Clayton, Richard N Jones, Ted R Miller, Jennifer E Johnson, Lauren M Weinstock, Sheryl Kubiak

“…We hypothesize that a combination of intake screening PAU and the ML model will be the optimal approach, in that the combination will be more accurate and can have practical application in this context. …”
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Improving the performance of machine learning algorithms for detection of individual pests and beneficial insects using feature selection techniques by Rabiu Aminu, Samantha M. Cook, David Ljungberg, Oliver Hensel, Abozar Nasirahmadi

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Predicting Geostationary 40–150 keV Electron Flux Using ARMAX (an Autoregressive Moving Average Transfer Function), RNN (a Recurrent Neural Network), and Logistic Regression: A Com... by L. E. Simms, N. Yu. Ganushkina, M. Van derKamp, M. Balikhin, M. W. Liemohn

“…Abstract We screen several algorithms for their ability to produce good predictive models of hourly 40–150 keV electron flux at geostationary orbit (data from GOES‐13) using solar wind, Interplanetary Magnetic Field, and geomagnetic index parameters that would be available for real time forecasting. …”
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