Explainable brain age prediction: a comparative evaluation of morphometric and deep learning pipelines by Maria Luigia Natalia De Bonis, Giuseppe Fasano, Angela Lombardi, Carmelo Ardito, Antonio Ferrara, Eugenio Di Sciascio, Tommaso Di Noia

“…This study addresses a critical gap by systematically comparing the interpretability of multiple XAI methods across distinct brain age prediction pipelines. …”
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Evaluation of a pragmatic approach to predicting COVID-19-positive hospital bed occupancy by Azeem Majeed, Thomas Woodcock, Paul Aylin, Julian Redhead, Derryn Lovett, Jacques Naude

“…Study objectives This study evaluates the feasibility and accuracy of a pragmatic approach to predicting hospital bed occupancy for COVID-19-positive patients, using only simple methods accessible to typical health system teams.Methods We used an observational forecasting design for the study period 1st June 2021 to –21st January 2022. …”
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Evaluation of first trimester serum double marker test in prediction of fetal growth restriction by Amrita Chaurasia, Shalini Singh, Kumari Shweta

“…Objectives: To evaluate the clinical utility of the first trimester serum double marker test in their ability to predict subsequent delivery of a fetal growth restriction (FGR) infant. …”
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Evaluation of machine learning techniques for real-time prediction of implanted lower limb mechanics by Chase Maag, Clare K. Fitzpatrick, Paul J. Rullkoetter

“…This study investigates the application of machine learning (ML) techniques for real-time prediction of knee joint mechanics.MethodsA validated finite element (FE) model of the lower limb was used to generate a dataset of knee joint kinematics, kinetics, and contact mechanics. …”
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Evaluation of Eight Decomposition-Hybrid Models for Short-Term Daily Reference Evapotranspiration Prediction by Yunfei Chen, Zuyu Liu, Ting Long, Xiuhua Liu, Yaowei Gao, Sibo Wang

“…These findings underscore the importance of selecting appropriate decomposition methods to balance high-frequency information and predictive accuracy, offering insights for improving ET_o forecasting in arid regions.…”
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Construction and Evaluation of a Domain-Related Risk Model for Prognosis Prediction in Colorectal Cancer by Xiangjun Cui, Yongqiang Xing, Guoqing Liu, Hongyu Zhao, Zhenhua Yang

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Transdiagnostic early warning score for psychiatric hospitalisation: development and evaluation of a prediction model by Seena Fazel, A John Rush, Maxime Taquet

“…Background The lack of an early warning score for psychiatric hospitalisation means that the decision to initiate preventative interventions is based solely on clinical judgement, which is prone to bias.Objective The objective is to develop and externally validate a transdiagnostic score that predicts psychiatric hospitalisation.Methods In this retrospective cohort study using deidentified electronic health records from 20 healthcare organisations in the NeuroBlu Data, we identified all patients with any of seven major psychiatric disorders with at least five Clinical Global Impressions of Severity and five Global Assessment of Functioning measured over a period of 6 consecutive months before any hospitalisation. …”
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Evaluation of NAPLES Prognostic Score to Predict Long-Term Mortality in Patients with Pulmonary Embolism by Süheyla Kaya, Veysi Tekin

“…<b>Methods:</b> The clinical data of patients who applied to the Dicle University Faculty of Medicine and were diagnosed with APE between March 2014 and April 2024 were evaluated retrospectively, with 436 patients aged 18 years and over included in the study. …”
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Analysis of peripheral blood variables in evaluation and prediction of adverse outcomes with hypertensive disorders of pregnancy by Yingdi Jiang, Lu Dai, Jiang Lin, Yanjie Zhou

“…Moreover, constructed a nomogram that can easily predict and evaluate the risk of adverse pregnancy outcomes in women with HDP.…”
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Comprehensive Evaluation of Bankruptcy Prediction in Taiwanese Firms Using Multiple Machine Learning Models by Hung V. Pham, Tuan Chu, Tuan M. Le, Hieu M. Tran, Huong T.K. Tran, Khanh N. Yen, Son V. T. Dao

“…From the results measured by evaluation metrics, the proposed model ANN with the combination of parameter tuning, feature selection algorithm, SMOTE-ENN, and optimal hyper-parameters demonstrates superior performance compared to traditional methods, achieving an F1 Score of 98.5% and an accuracy of 98.6%. …”
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Development and evaluation of a machine learning model for osteoporosis risk prediction in Korean women by Minkyung Je, Seunghyeon Hwang, Suwon Lee, Yoona Kim

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Evaluation of multiple machine learning models predicting the results of hybrid imaging in primary hyperparathyroidism by Anna Drynda, Jacek Podlewski, Karolina Kucharczyk, Grzegorz Sokołowski, Anna Sowa-Staszczak, Alicja Hubalewska-Dydejczyk, Małgorzata Trofimiuk- Müldner

“…MATERIAL AND METHODS: Development and evaluation of logistic regression (LR), classification trees utilizing the classification and regression trees (CART) algorithm, random forest (RF), and boosted trees employing XGBoost (XGB) predictive models. …”
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MELD score predicts outcomes in patients with advanced heart failure: A longitudinal evaluation by Francesco Curcio, Cristiano Amarelli, Rosaria Chiappetti, Irene Mattucci, Veronica Flocco, Mahmoud Issa Rammal, Ciro Abete, Francesca Mazzella, Ciro Maiello, Pasquale Abete, Francesco Cacciatore

“…The objectives of this study were to evaluate the effect of the model for end‐stage liver disease (MELD) score and its variations in predicting adverse outcomes [death, urgent heart transplant, and left ventricular assist device (LVAD) implant] among patients with AHF to assess the clinical associations of the MELD score in this population and to compare the efficacy of this tool with other prognostic scores in AHF. …”
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Comparison of the Methods for Predicting the Critical Temperature and Critical Pressure of Petroleum Fractions and Individual Hydrocarbons by Evdokia Sotirova, Svetlin Vasilev, Dicho Stratiev, Ivelina Shishkova, Sotir Sotirov, Radoslava Nikolova, Anife Veli, Veselina Bureva, Krassimir Atanassov, Vanya Georgieva, Denis Stratiev, Svetoslav Nenov

“…Consequently, effective methods for predicting critical temperature and pressure must account for a wide range of compositional scenarios. …”
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Prediction of cholesterol level in patients with myocardial infarction based on medical data mining methods by Cemil Colak, Mehmet C. Colak, Necip Ermis, Nevzat Erdil, Ramazan Ozdemir

“…The current study was carried out to predict the cholesterol level in patients with MI usingdata mining methods, artificial neural networks (ANNs) and support vector machine (SVM) models. …”
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Machine learning methods for predicting residual strength in corroded oil and gas steel pipes by Qiankun Wang, Hongfang Lu

“…We analyze various machine learning models, data preprocessing methods, and evaluation metrics used in existing research. …”
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Multistage deep learning methods for automating radiographic sharp score prediction in rheumatoid arthritis by Hajar Moradmand, Lei Ren

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Prediction of alkali-silica reaction expansion of concrete using explainable machine learning methods by Yasitha Alahakoon, Hirushan Sajindra, Ashen Krishantha, Janaka Alawatugoda, Imesh U. Ekanayake, Upaka Rathnayake

“…Abstract Traditionally, ASR expansion is determined using experimental and finite element method (FEM) based numerical modelling. However, these methods are time-consuming and computationally costly, which makes ASR prediction challenging. …”
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Health literacy prediction models based on machine learning methods: a scoping review by PAN Xiang, TONG Yingge, LI Yixuan, NI Ke, CHENG Wenqian, XIN Mengyu, HU Yuying

“…Totally 48 health literacy prediction models were involved, and 25 had a high risk of bias (52.08%), with major issues focusing on missing value handling, predictor selection and model evaluation methods. …”
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Research on optimal selection of runoff prediction models based on coupled machine learning methods by Xing Wei, Mengen Chen, Yulin Zhou, Jianhua Zou, Libo Ran, Ruibo Shi

“…The study first selects artificial neural network (ANN) and support vector machine (SVM) as the base models. Then, it evaluates and selects from three time-series decomposition methods. …”
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