<p>Accurate prediction in structured hospital monitoring data is challenging because inpatient datasets are high-dimensional and often contain redundant features and suboptimal hyperparameter settings. This problem is important because unreliable prediction can limit the effectiveness of hospital monitoring and clinical decision support. To address this, this study proposes a unified optimization framework that integrates the Al-Biruni Earth Radius (BER) metaheuristic with the Feature-Transformed Learning Model (FTLM) for both binary feature selection and continuous hyperparameter optimization. BER is first applied in a discrete search space to identify informative subsets of vital-sign, demographic, clinical, and temporal variables from the <i>Patient Vital Signs and Event Tracking</i> dataset, and then in a continuous space to tune FTLM hyperparameters under the same computational budget used for competing optimizers, including GWO, PSO, BA, WAO, SBO, SCA, FA, GA, and SAO. At baseline, FTLM achieved a mean squared error (MSE) of 0.012028 and <InlineEquation ID="IEq1"><EquationSource Format="TEX">\(R^2\)</EquationSource></InlineEquation> of 0.782413. After BER-based feature selection, performance improved to an MSE of <InlineEquation ID="IEq2"><EquationSource Format="TEX">\(9.80 \times 10^{-3}\)</EquationSource></InlineEquation> and <InlineEquation ID="IEq3"><EquationSource Format="TEX">\(R^2\)</EquationSource></InlineEquation> of 0.860654, with correlation of 0.848181 and Nash–Sutcliffe efficiency of 0.879577. Following BER-driven hyperparameter optimization, FTLM attained an MSE of <InlineEquation ID="IEq4"><EquationSource Format="TEX">\(7.43 \times 10^{-7}\)</EquationSource></InlineEquation>, RMSE of <InlineEquation ID="IEq5"><EquationSource Format="TEX">\(8.62 \times 10^{-4}\)</EquationSource></InlineEquation>, correlation coefficient of 0.955593543, <InlineEquation ID="IEq6"><EquationSource Format="TEX">\(R^2\)</EquationSource></InlineEquation> of 0.961124043, and Willmott Index of 0.963281686, achieving the strongest empirical performance among the evaluated optimizers under the same experimental setting. To further assess generalizability, an external validation experiment was conducted on an independent Human Vital Sign Dataset containing 200,000 samples, where BER + FTLM again achieved the strongest empirical performance among the evaluated optimizers. These findings show that BER provides stable convergence, reduced variance, and strong predictive alignment for structured clinical data modeling.</p>

Structured vital sign prediction in hospital environments via an Al-Biruni earth radius optimization–driven unified metaheuristic framework

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2026年5月20日

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Sarah A. Alzakari,
Marwa M. Eid,
Amel Ali Alhussan,
S. K. Towfek

摘要

Accurate prediction in structured hospital monitoring data is challenging because inpatient datasets are high-dimensional and often contain redundant features and suboptimal hyperparameter settings. This problem is important because unreliable prediction can limit the effectiveness of hospital monitoring and clinical decision support. To address this, this study proposes a unified optimization framework that integrates the Al-Biruni Earth Radius (BER) metaheuristic with the Feature-Transformed Learning Model (FTLM) for both binary feature selection and continuous hyperparameter optimization. BER is first applied in a discrete search space to identify informative subsets of vital-sign, demographic, clinical, and temporal variables from the Patient Vital Signs and Event Tracking dataset, and then in a continuous space to tune FTLM hyperparameters under the same computational budget used for competing optimizers, including GWO, PSO, BA, WAO, SBO, SCA, FA, GA, and SAO. At baseline, FTLM achieved a mean squared error (MSE) of 0.012028 and \(R^2\) of 0.782413. After BER-based feature selection, performance improved to an MSE of \(9.80 \times 10^{-3}\) and \(R^2\) of 0.860654, with correlation of 0.848181 and Nash–Sutcliffe efficiency of 0.879577. Following BER-driven hyperparameter optimization, FTLM attained an MSE of \(7.43 \times 10^{-7}\), RMSE of \(8.62 \times 10^{-4}\), correlation coefficient of 0.955593543, \(R^2\) of 0.961124043, and Willmott Index of 0.963281686, achieving the strongest empirical performance among the evaluated optimizers under the same experimental setting. To further assess generalizability, an external validation experiment was conducted on an independent Human Vital Sign Dataset containing 200,000 samples, where BER + FTLM again achieved the strongest empirical performance among the evaluated optimizers. These findings show that BER provides stable convergence, reduced variance, and strong predictive alignment for structured clinical data modeling.