A modified weighted mean of vectors optimizer for Chronic Kidney disease classification

Houssein, Essam H.; Sayed, Awny

doi:10.1016/j.compbiomed.2023.106691

Cited by 18 publications

(8 citation statements)

References 43 publications

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“…Li et al developed an accurate and non-invasive diagnostic model for tuberculous pleural effusion, and Fei et al contributed to the field by creating a diagnostic model for brain diseases, showcasing the effectiveness of advanced machine learning methodologies ( Li et al, 2018 ; Fei et al, 2020 ). To optimize the performance of disease classification, Xia and Houssein et al introduced two optimization techniques, further enhancing the precision and reliability of the diagnostic models ( Xia et al, 2022 ; Houssein and Sayed, 2023 ). Zhao et al dedicated the development of accurate brain magnetic resonance images segmentation, while Emam et al focused on refining retinal vessel segmentation algorithms ( Zhao et al, 2020 ; Emam et al, 2023 ).…”

Section: Discussionmentioning

confidence: 99%

Ensemble machine learning prediction of hyperuricemia based on a prospective health checkup population

Zhang,

et al. 2024

Front. Physiol.

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Objectives: An accurate prediction model for hyperuricemia (HUA) in adults remain unavailable. This study aimed to develop a stacking ensemble prediction model for HUA to identify high-risk groups and explore risk factors.Methods: A prospective health checkup cohort of 40899 subjects was examined and randomly divided into the training and validation sets with the ratio of 7:3. LASSO regression was employed to screen out important features and then the ROSE sampling was used to handle the imbalanced classes. An ensemble model using stacking strategy was constructed based on three individual models, including support vector machine, decision tree C5.0, and eXtreme gradient boosting. Model validations were conducted using the area under the receiver operating characteristic curve (AUC) and the calibration curve, as well as metrics including accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score. A model agnostic instance level variable attributions technique (iBreakdown) was used to illustrate the black-box nature of our ensemble model, and to identify contributing risk factors.Results: Fifteen important features were screened out of 23 clinical variables. Our stacking ensemble model with an AUC of 0.854, outperformed the other three models, support vector machine, decision tree C5.0, and eXtreme gradient boosting with AUCs of 0.848, 0.851 and 0.849 respectively. Calibration accuracy as well as other metrics including accuracy, specificity, negative predictive value, and F1 score were also proved our ensemble model’s superiority. The contributing risk factors were estimated using six randomly selected subjects, which showed that being female and relatively younger, together with having higher baseline uric acid, body mass index, γ-glutamyl transpeptidase, total protein, triglycerides, creatinine, and fasting blood glucose can increase the risk of HUA. To further validate our model’s applicability in the health checkup population, we used another cohort of 8559 subjects that also showed our ensemble prediction model had favorable performances with an AUC of 0.846.Conclusion: In this study, the stacking ensemble prediction model for HUA was developed, and it outperformed three individual models that compose it (support vector machine, decision tree C5.0, and eXtreme gradient boosting). The contributing risk factors were identified with insightful ideas.

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Section: Discussionmentioning

confidence: 99%

Ensemble machine learning prediction of hyperuricemia based on a prospective health checkup population

Zhang,

et al. 2024

Front. Physiol.

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“…It consists of four key phases: initialization (described above), updating rule, vector combining, and local search. Due to its solid mathematical concepts, INFO has been demonstrated to be effective in several applications, such as engineering optimization 44 , image classification tasks 45 , and feature selection 46 . This section briefly overviews some of the main operators and equations used in INFO, with more detailed information available in 37 .…”

Section: Methodsmentioning

confidence: 99%

Integrated metaheuristic algorithms with extreme learning machine models for river streamflow prediction

Van Thieu,

Nguyen,

Sherif

et al. 2024

Sci Rep

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Accurate river streamflow prediction is pivotal for effective resource planning and flood risk management. Traditional river streamflow forecasting models encounter challenges such as nonlinearity, stochastic behavior, and convergence reliability. To overcome these, we introduce novel hybrid models that combine extreme learning machines (ELM) with cutting-edge mathematical inspired metaheuristic optimization algorithms, including Pareto-like sequential sampling (PSS), weighted mean of vectors (INFO), and the Runge–Kutta optimizer (RUN). Our comparative assessment includes 20 hybrid models across eight metaheuristic categories, using streamflow data from the Aswan High Dam on the Nile River. Our findings highlight the superior performance of mathematically based models, which demonstrate enhanced predictive accuracy, robust convergence, and sustained stability. Specifically, the PSS-ELM model achieves superior performance with a root mean square error of 2.0667, a Pearson’s correlation index (R) of 0.9374, and a Nash–Sutcliffe efficiency (NSE) of 0.8642. Additionally, INFO-ELM and RUN-ELM models exhibit robust convergence with mean absolute percentage errors of 15.21% and 15.28% respectively, a mean absolute errors of 1.2145 and 1.2105, and high Kling-Gupta efficiencies values of 0.9113 and 0.9124, respectively. These findings suggest that the adoption of our proposed models significantly enhances water management strategies and reduces any risks.

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“…The authors conducted a comprehensive comparison of HGSO with seven recently published algorithms, namely Snow Ablation Optimizer (SAO) [176], Liver Cancer Algorithm (LCA) [177], Light Spectrum Optimizer (LSO) [178], Transient Search Algorithm (TSO) [179], Arithmetic Optimization Algorithm (AOA) [40], Coati Optimization Algorithm (COA) [180], and Thermal Exchange Optimization (TEO) [181]. To ensure fair and statistically significant benchmarking, considering the stochastic nature of these methods, the authors performed 30 independent runs with a maximum of 1000 iterations for each method.…”

Section: B Parameter Settingmentioning

confidence: 99%

Advances in Henry Gas Solubility Optimization: A Physics-Inspired Metaheuristic Algorithm With Its Variants and Applications

El-Shorbagy,

Bouaouda,

Nabwey

et al. 2024

IEEE Access

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The Henry Gas Solubility Optimization (HGSO) is a physics-based metaheuristic inspired by Henry's law, which describes the solubility of the gas in a liquid under specific pressure conditions. Since its introduction by Hashim et al. in 2019, HGSO has gained significant attention for its unique features, including minimal adaptive parameters and a balanced exploration-exploitation trade-off, leading to favorable convergence. This study provides an up-to-date survey of HGSO, covering the walk through the historical development of HGSO, its modifications, and hybridizations with other algorithms, showcasing its adaptability and potential for synergy. Recent variants of HGSO are categorized into modified, hybridized, and multi-objective versions, and the review explores its main applications, demonstrating its effectiveness in solving complex problems. The evaluation includes a discussion of the algorithm's strengths and weaknesses. This comprehensive review, featuring graphical and tabular comparisons, not only indicates potential future directions in the field but also serves as a valuable resource for researchers seeking a deep understanding of HGSO and its advanced versions. As physics-based metaheuristic algorithms gain prominence for solving intricate optimization problems, this study provides insights into the adaptability and applications of HGSO across diverse domains.

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A modified weighted mean of vectors optimizer for Chronic Kidney disease classification

Cited by 18 publications

References 43 publications

Ensemble machine learning prediction of hyperuricemia based on a prospective health checkup population

Ensemble machine learning prediction of hyperuricemia based on a prospective health checkup population

Integrated metaheuristic algorithms with extreme learning machine models for river streamflow prediction

Advances in Henry Gas Solubility Optimization: A Physics-Inspired Metaheuristic Algorithm With Its Variants and Applications

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