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  1. 21

    Evaluation of predictive performance of modeling hyperuricemia using medical big data: comparison of data preprocessing methods by Luwei Li, Xian Huang, Cijin Yan, Shuzhan He, Sishuai Cheng, WenJie Yang

    Published 2025-04-01
    “…Abstract Background Using medical big data from two large-scale populations, a prediction model for continuous variables of raw data and a prediction model for categorical variables after assignment were constructed to evaluate the performance of the two forms of data preprocessing models. …”
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    Article
  2. 22

    Evaluating the Stress State and the Load-Bearing Fraction as Predicted by an In Vivo Parameter Identification Method for the Abdominal Aorta by Jerker Karlsson, Jan-Lucas Gade, Carl-Johan Thore, Carl-Johan Carlhäll, Jan Engvall, Jonas Stålhand

    Published 2025-01-01
    “…<b>Methods:</b> Our team has evaluated an in vivo parameter identification method through in silico experiments involving finite element models and demonstrated good agreement with the parameters of a healthy abdominal aorta. …”
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    Article
  3. 23

    A dissimilarity-adaptive cross-validation method for evaluating geospatial machine learning predictions with clustered samples by Yanwen Wang, Mahdi Khodadadzadeh, Raúl Zurita-Milla

    Published 2025-12-01
    “…Results showed that DA-CV provided the most accurate evaluations in 85% of scenarios. DA-CV effectively overcomes the common limitations of random and spatial CV methods, such as only considering a part of predictions in the evaluation. …”
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    Article
  4. 24

    Evaluation of Four Methods for Predicting Carbon Stocks of Korean Pine Plantations in Heilongjiang Province, China. by Huilin Gao, Lihu Dong, Fengri Li, Lianjun Zhang

    Published 2015-01-01
    “…The first method predicted carbon stocks directly by the compatible carbon stocks models (Method 1). …”
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    Article
  5. 25

    Evaluation and prediction of coal seam mining mode: Coefficient of Variation-TOPSIS and CNN-NGO methods by Haixiong Li, Fei Wang

    Published 2025-01-01
    “…This study explores and validates an integrated evaluation system that enhances the accuracy of predicting coal seam mining mode by comparing traditional evaluation methods with machine-learning techniques. …”
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    Article
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    Evaluation of the KDS 14 Draft Design Method for Predicting the Shear Strength of Prestressed Concrete Beams by Ngoc Hieu Dinh, Si-Hyun Kim, Kyoung-Kyu Choi

    Published 2025-06-01
    “…This study evaluated the applicability of the KDS 14 draft design method, which is based on compression zone failure theory, for predicting the shear strength of slender PSC beams. …”
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    Article
  8. 28

    Approaches, Relevant Topics, and Internal Method for Uncertainty Evaluation in Predictions of Thermal-Hydraulic System Codes by Alessandro Petruzzi, Francesco D'Auria

    Published 2008-01-01
    “…The needs come from the imperfection of computational tools, on the one side, and the interest in using such a tool to get more precise evaluation of safety margins. The paper reviews the salient features of three independent approaches for estimating uncertainties associated with predictions of complex system codes. …”
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    Machine Learning System for Predicting Cardiovascular Disorders in Diabetic Patients by A. Mayya, H. Solieman

    Published 2022-09-01
    “…Feature selection methods were used to derive the most significant indicators for predicting CVD risk in diabetic patients. …”
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    A Prediction Method for Postconstruction Settlement of Pile-Soil Composite Subgrade Based on Fuzzy Comprehensive Evaluation by Hao Shan, Guanghui Jiang, Yajing Chang, Junli Cheng, Baoning Hong, Shengcheng Wang

    Published 2021-01-01
    “…This paper presents a postconstruction settlement prediction method for pile-soil composite subgrade based on the multilevel fuzzy comprehensive evaluation principle. …”
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    Article
  19. 39

    Enhancing diabetes risk prediction: A comparative evaluation of bagging, boosting, and ensemble classifiers with SMOTE oversampling by Rabia Asif, Darshana Upadhyay, Marzia Zaman, Srini Sampalli

    Published 2025-01-01
    “…This study explores advanced machine learning techniques, specifically bagging, boosting, and ensemble methods to improve diabetes risk prediction. Using three diverse datasets, namely, the Centers for Disease Control and Prevention (CDC) Diabetes Health Indicators dataset, the Early Stage Diabetes Risk Prediction System (ESDRP) dataset, and the PIMA Indian Diabetes dataset are utilized to evaluate the adaptability and robustness of the proposed models. …”
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    Article
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