Mortality impact, risks, and benefits of general population screening for ovarian cancer: the UKCTOCS randomised controlled trial by Usha Menon, Aleksandra Gentry-Maharaj, Matthew Burnell, Andy Ryan, Jatinderpal K Kalsi, Naveena Singh, Anne Dawnay, Lesley Fallowfield, Alistair J McGuire, Stuart Campbell, Steven J Skates, Mahesh Parmar, Ian J Jacobs

“…Methods Participants Postmenopausal average-risk women, aged 50–74, with intact ovaries and no previous ovarian or current non-ovarian cancer. …”
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Using machine learning and single nucleotide polymorphisms for improving rheumatoid arthritis risk Prediction in postmenopausal women. by Yingke Xu, Qing Wu

“…However, few studies have used genetic variants to predict RA risk. This study aimed to enhance RA risk prediction by leveraging single nucleotide polymorphisms (SNPs) through machine-learning algorithms, utilizing Women's Health Initiative data. …”
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Predicting the risk of postoperative avascular necrosis in patients with talar fractures based on an interpretable machine learning model by Jian Zhang, Jian Zhang, Jian Zhang, Jihai Xu, Jihai Xu, Jiapei Yu, Jiapei Yu, Jiapei Yu, Hong Chen, Hong Chen, Xin Hong, Songou Zhang, Xin Wang, Xin Wang, Chengchun Shen, Chengchun Shen, Chengchun Shen

“…Univariate and multivariable logistic regression identified six independent risk factors including body mass index (BMI), fracture classification, concomitant ipsilateral foot and ankle fractures, smoking, quality of fracture reduction, and fracture type. …”
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Deep Learning‐Guided Urban Climate Risk Mitigation Through Optimal Spatial Allocation of Green and Cool Roofs by JiHyun Kim, Suyeon Choi, Mahdi Panahi, Hocheol Seo, Yeonjoo Kim

“…These indices were used to test four deep learning algorithms: UNet, UNet++, UNet3+, and Multi‐ResUNet. …”
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Development and interpretation of a machine learning risk prediction model for post-stroke depression in a Chinese population by Xia Zhong, Tianen Zhao, Shimeng Lv, Guangheng Zhang, Jing Li, Donghai Liu, Huachen Jiao

“…After selecting the core predictors of PSD using LASSO regression dimension reduction, six machine learning (ML) algorithms were used to statistically model the risk prediction of PSD. …”
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Comparative review of intelligent structural safety in building seismic risk mitigation utilizing an integrated artificial intelligence controller by Normaisharah Mamat, Rawad Abdulghafor, Sherzod Turaev, Fitri Yakub

“…A systematic analysis of the literature is performed to identify and evaluate prior research on AI controllers employed to reduce seismic risk in structures. The research highlights the influence of integrated AI controllers on control systems, examining several AI controllers, including machine learning algorithms, neural networks, and evolutionary algorithms concerning structural safety. …”
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A smarter approach to liquefaction risk: harnessing dynamic cone penetration test data and machine learning for safer infrastructure by Shubhendu Vikram Singh, Sufyan Ghani

“…ML models, including Support Vector Machine (SVM) optimized with Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Genetic Algorithm (GA), and Firefly Algorithm (FA), were employed to predict the e/qd ratio using key geotechnical parameters, such as fine content, peak ground acceleration, reduction factor, and penetration rate. …”
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Hybrid metaheuristic optimization for detecting and diagnosing noncommunicable diseases by Saleem Malik, S. Gopal Krishna Patro, Chandrakanta Mahanty, Saravanapriya Kumar, Ayodele Lasisi, Quadri Noorulhasan Naveed, Anjanabhargavi Kulkarni, Abdulrajak Buradi, Addisu Frinjo Emma, Naoufel Kraiem

“…The proposed framework introduces novel hybrid algorithms, including the Hierarchical Genetic Multiple Reduct Selection Algorithm (H-GMRA) and the Customized Function-based Particle Swarm Optimization with Rough Set Theory for NCD Feature Selection (CPSO-RST-NFS). …”
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Impact of the China's new energy market on carbon price fluctuation risk: Evidence from seven pilot carbon markets by Ruo-Yang Pu, Qiao-Mei Liang, Yi-Ming Wei, Song-Yang Yan, Xiang-Yu Wang, De-Hua Li, Chen Yi, Chang-Jing Ji

“…Since China implemented its carbon trading mechanism, trading risks arising from unstable carbon prices have significantly reduced its emission-reduction efficiency. …”
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Econometric modeling for proactive risk management of financial failure in Moroccan SMEs: a stepwise logistic regression approach in python by Dina Ait Lahcen, Nour-Eddin Amghar

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Quantum-Inspired Hyperheuristic Framework for Solving Dynamic Multi-Objective Combinatorial Problems in Disaster Logistics by Kassem Danach, Hassan Harb, Louai Saker, Ali Raad

“…Notably, our method achieves a 9.6% reduction in total travel cost, a 6.5% decrease in cumulative risk exposure, and a 4.7% increase in priority-weighted demand satisfaction when benchmarked against existing techniques. …”
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Modifiable Factors and 10‐Year and Lifetime Risk of Cardiovascular Disease in Adults With New‐Onset Diabetes: The Kailuan Cohort Study by Shouling Wu, Yuntao Wu, Yi Ning, Xiang Peng, Haiyan Zhao, Jun Feng, Liming Lin, Chunyu Ruan, Shuohua Chen, Jinwei Tian, Cheng Jin

“…Lifetime CVD risk reductions were 9% (HR, 0.91 [95% CI, 0.81–1.03]), 27% (HR, 0.73 [95% CI, 0.62–0.85]), and 61% (HR, 0.39 [95% CI, 0.29–0.52]), respectively. …”
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MINIMIZATION OF RISK OF THE ERRONEOUS DECISION IN THE ASSESSMENT OF THE IMPORTANCE OF STATISTICAL RELATIONS OF TECHNICAL AND ECONOMIC INDICATORS OF THE OBJECTS OF ELECTRIC POWER SY... by E. M. Farhadzadeh, A. Z. Muradaliyev, Yu. Z. Farzaliyev, T. K. Rafiyeva, S. A. Abdullayeva

“…The novelty consists in the application of fiducial approach; the calculation of critical values are fulfilled with the aid of computer technologies of simulation of possible realizations of the correlation coefficients for the two assumptions, viz. technical and economic indicators of the independent and dependent; simulation is fulfilled with the method of solving the “inverse problem”, which enables the possible implementation of the correlation coefficients for the really dependent and independent samples of random variables at a given sample size; the developed algorithms and programs for calculation made it possible to obtain the critical values of correlation coefficients for independent and dependent samples; in conditions of the sameness of the consequences of erroneous decisions it is proposed to make a decision not based on critical value but based on the boundary values of the correlation coefficients that correspond to the minimum total risk of erroneous decisions; the exemplification of the recommendations application was made on example of technical and economic parameters of boilers of power units of 300 MWt. …”
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Evaluation of Low-dose Computed Tomography Images Reconstructed Using Artificial Intelligence-based Adaptive Filtering for Denoising: A Comparison with Computed Tomography Reconstr... by Suyash Kulkarni, Vasundhara Patil, Aniruddha Nene, Nitin Shetty, Amitkumar Choudhari, Akansha Joshi, CS Pramesh, Akshay Baheti, Kalpesh Mahadik

“…Purpose: Awareness of radiation-induced risk led to the development of various dose optimization techniques in iterative reconstruction (IR) algorithms and deep learning algorithms to improve low-dose image quality. …”
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Clinical, Echocardiographic and Laboratory Cardiac Monitoring in Breast Cancer Patients Undergoing Chemotherapy with Anthracycline Agents: A Pilot Study on Cardioprotective Algorit... by Indah Puspita

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Modifiable factors and 10-year and lifetime cardiovascular disease risk in adults with new-onset hypertension: insights from the Kailuan cohort by Shouling Wu, Yanxiu Wang, Jiangshui Wang, Jun Feng, Furong Li, Liming Lin, Chunyu Ruan, Zhifang Nie, Jinwei Tian, Cheng Jin

“…Eight modifiable factors were assessed using the American Heart Association’s Life’s Essential 8 algorithm. We followed participants for incident CVD until December 2020, estimating 10-year and lifetime (age 25–95) CVD risks using the Fine-Gray competing risks model. …”
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Inflammation-Driven Prognosis in Advanced Heart Failure: A Machine Learning-Based Risk Prediction Model for One-Year Mortality by Zhou M, Du X

“…AdHF patients admitted to the ICU and cardiology department from January 2015 to December 2023 were included with a one-year follow-up. 52 variables potentially affecting prognosis were incorporated. The LASSO algorithm was used for feature selection and dimensionality reduction. …”
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Proactive dynamic flooding regulations for river basins in China’s arid and semi-arid region of Xinjiang by Xintong Gong, Qiang Zhang, Senlin Tang, Yungang Bai, Vijay P. Singh, Zhenlin Lu

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Ways to Reduce In-Hospital Mortality in Patients with Cardiogenic Shock in Acute Coronary Syndrome by G. V. Artamonova, V. Yu. Kheraskov, E. V. Grigoryev, O. V. Kushch, D. V. Kryuchkov, L. S. Barbarash

“…The management is based on the principle of continuity of care, by applying the well-defined activity algorithms through valid information exchange and risk stratification for poor outcomes of ACS. …”
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Explainable AutoML models for predicting the strength of high-performance concrete using Optuna, SHAP and ensemble learning by Muhammad Salman Khan, Tianbo Peng, Tianbo Peng, Muhammad Adeel Khan, Asad Khan, Mahmood Ahmad, Mahmood Ahmad, Kamran Aziz, Mohanad Muayad Sabri Sabri, N. S. Abd EL-Gawaad

“…This approach provides civil engineers with a robust and interpretable tool for optimizing HPC properties, reducing experimentation costs, and supporting enhanced decision-making in structural design, risk assessment, and other applications.…”
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