Physiology-Aware Ensemble Learning for Short-Horizon Glucose Prediction in Diabetic Patients Using Matlab

Author : Waqas Javaid

Abstract

This study presents a comprehensive machine learning framework for predicting glucose levels in diabetic patients six hours ahead. Using synthetically generated physiological data incorporating insulin sensitivity, beta-cell function, and meal patterns, we engineered advanced temporal, spectral, and entropy-based features [1]. Four models Ridge Regression, Support Vector Regression, Gradient Boosting, and Neural Networks were trained and compared, with an ensemble model achieving superior performance [2]. The framework demonstrates high predictive accuracy (RMSE: 1.42 mmol/L, R²: 0.89) and strong clinical safety, with 92.3% of predictions falling in Zones A and B of the Clarke Error Grid Analysis. The results highlight the importance of integrating physiological parameters and multi-domain feature extraction for reliable glucose forecasting, offering a robust tool for personalized diabetes management and hypoglycemia prevention [3].

1. Introduction

Diabetes mellitus, characterized by chronic hyperglycemia, represents a global health crisis affecting over half a billion individuals, with precise glucose management being paramount to prevent debilitating complications [4].

Traditional management relies on retrospective continuous glucose monitoring (CGM) data, offering limited foresight into impending glycemic excursions, particularly dangerous hypoglycemic events [5]. The advent of predictive algorithms promises a paradigm shift from reactive to proactive care, enabling preventative interventions. However, accurate glucose forecasting remains a significant challenge due to the complex, non-linear interplay of physiological factors like insulin sensitivity, carbohydrate absorption, and beta-cell function. Current models often rely solely on historical glucose values, overlooking these critical underlying dynamics, which limits their generalizability and robustness in free-living conditions. This study addresses these limitations by developing a comprehensive, physiologically-informed machine learning framework for multi-hour ahead prediction [6]. We generate a synthetic yet realistic dataset simulating key patient-specific parameters and meal-insulin interactions. Beyond simple temporal features, we extract advanced metrics from spectral, entropy, and variability domains to capture the complex signature of glucose dynamics [7]. We implement and compare a suite of models Ridge Regression, Support Vector Regression, Gradient Boosting, and Neural Networks culminating in a weighted ensemble model.

Table 1: Clarke Error Grid Zone Distribution (Ensemble Model)

Performance is rigorously evaluated using both statistical accuracy (RMSE, R²) and, crucially, clinical safety metrics via Clarke Error Grid Analysis [8]. This integrated approach aims to deliver a reliable, clinically-actionable forecasting tool that moves personalization beyond insulin dosing calculators towards truly anticipatory diabetes management.

1.1 The Global Challenge of Diabetes Management

Diabetes mellitus, a chronic metabolic disorder characterized by dysregulated glucose homeostasis, imposes a substantial and growing burden on global healthcare systems. Its management necessitates constant vigilance to maintain blood glucose within a safe, narrow range, as prolonged deviations lead to severe microvascular and macrovascular complications. Despite technological advances like continuous glucose monitors (CGMs) and insulin pumps, achieving optimal glycemic control remains an elusive goal for many patients. A primary obstacle is the inherently dynamic and multivariate nature of glucose regulation, influenced by meals, physical activity, stress, medication, and individual physiological parameters [9]. Current standard-of-care practices are predominantly reactive, where therapeutic adjustments are made in response to measured glucose levels, often after problematic excursions have already occurred. This reactive paradigm leaves patients vulnerable to acute dangers like hypoglycemia, which can cause confusion, seizures, or loss of consciousness. Consequently, there is a pressing, unmet clinical need for tools that can transition diabetes management from a reactive to a proactive, predictive model [10]. Such tools would forecast future glucose trends, enabling preemptive action to mitigate risks before they manifest. This study is positioned within this crucial endeavor, aiming to develop a sophisticated computational framework for accurate glucose prediction. The ultimate goal is to enhance patient safety, improve quality of life, and reduce the long-term complications associated with imperfect glycemic control.

1.2 The Complex Physiology and Limitations of Current Predictive Models

Accurate glucose prediction is fundamentally challenged by the complex physiology of glucose-insulin dynamics, which involves delayed, non-linear interactions across multiple biological compartments. Key patient-specific factors, such as insulin sensitivity, beta-cell function, insulin resistance, and carbohydrate-to-insulin ratios, create immense inter-individual variability in glycemic responses. Traditional time-series forecasting models, which often rely solely on sequences of past glucose values, fail to capture these underlying physiological mechanisms, limiting their accuracy and generalizability [11]. While data-driven machine learning approaches have shown promise, many existing models treat glucose prediction as a “black-box” problem, using historical CGM traces without incorporating meaningful biological features or contextual data like meal timing and insulin delivery. This omission of mechanistic context restricts the model’s ability to adapt to an individual’s unique metabolic profile or to unexpected physiological changes. Furthermore, the scarcity of large, high-quality, and comprehensively annotated real-world datasets poses a significant barrier to model development and validation. Many studies are constrained by small sample sizes or lack crucial ground-truth data for variables like exact meal composition and insulin dosing [12]. This data gap necessitates innovative approaches for generating realistic synthetic datasets that can simulate the complex interactions between physiology, behavior, and therapeutics. Our work directly addresses this by constructing a physiologically-plausible synthetic cohort, thereby providing a robust and controlled environment for model development and testing before clinical deployment.

1.3 Proposing an Integrated, Physiology-Informed Machine Learning Framework

To overcome the limitations of existing approaches, this study proposes a novel, integrated framework that merges principles from physiological modeling with state-of-the-art machine learning techniques. We hypothesize that prediction accuracy and clinical utility can be significantly enhanced by explicitly modeling key physiological processes and extracting a rich set of engineered features that describe glucose dynamics from multiple perspectives [13]. Our methodology begins with the generation of a synthetic patient cohort, where each virtual patient is defined by a unique set of physiological parameters (e.g., insulin sensitivity, basal rate) and subjected to realistic meal and insulin schedules. This allows us to simulate the core cause-and-effect relationships that drive glucose variability in a controlled yet complex manner. From the resulting glucose time series, we move beyond simple rolling statistics to engineer a comprehensive feature set. This includes time-domain metrics (e.g., mean, standard deviation, time-in-ranges), advanced variability indices (e.g., MAGE, CONGA), spectral features derived from Fourier analysis to capture cyclical patterns, and entropy measures to quantify signal complexity and predictability [14]. By creating this multi-faceted feature representation, we provide the predictive models with a much richer information landscape that encodes both the state and the dynamic behavior of the patient’s glucose regulation system, enabling more informed and accurate forecasts.

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1.4 Model Development, Ensemble Strategy, and Clinical Validation

With this rich feature set, we develop and compare a suite of diverse machine learning models, each chosen for its complementary strengths: Ridge Regression for stability and interpretability, Support Vector Regression for handling non-linearity, Gradient Boosting for capturing complex feature interactions, and Neural Networks as flexible function approximators [15]. Recognizing that no single algorithm may be optimal for all predictive scenarios, we employ an ensemble strategy, combining the predictions from these individual models through a weighted averaging scheme. This ensemble approach is designed to improve robustness, reduce variance, and leverage the collective intelligence of the model portfolio.

Table 2: Model Performance Metrics

Crucially, model performance is evaluated not only by standard statistical metrics like Root Mean Square Error (RMSE) and the coefficient of determination (R²) but also, and most importantly, by clinical validity metrics. We utilize the Clarke Error Grid Analysis (EGA), the gold-standard clinical tool for assessing the accuracy of glucose estimates, which categorizes prediction errors based on their potential to lead to clinically erroneous treatment decisions [16]. This dual-lens evaluation ensures the framework is not just mathematically accurate but also safe and actionable in a clinical context. By integrating physiological simulation, advanced feature engineering, a multi-model ensemble, and rigorous clinical validation, this work aims to deliver a significant step forward in creating reliable, personalized, and trustworthy tools for predictive glucose management in diabetes care.

1.5 Bridging the Gap Between Algorithmic Performance and Clinical Actionability

While achieving low prediction error is a necessary technical benchmark, the true test of a glucose forecasting system lies in its ability to drive positive clinical outcomes. A model must not only be accurate on average but also reliable in critical situations, such as impending hypoglycemia or rapid postprandial rises [17]. This requires moving beyond aggregate metrics to examine performance across different glycemic ranges and physiological states. Our framework explicitly incorporates this clinical perspective by analyzing prediction accuracy stratified by glucose levels and by calculating the percentage of time spent in target, hypoglycemic, and hyperglycemic ranges. Furthermore, we evaluate the model’s practical utility by assessing its lead time for detecting critical thresholds, providing a tangible measure of how much advance warning a patient or clinician might receive [18]. This step is vital for translating a computational output into a meaningful clinical alert or a recommended intervention within a decision support system. By designing the evaluation protocol with the end-user the patient and healthcare provider in mind, we ensure the research aligns with real-world needs. The goal is to create a tool that seamlessly integrates into the clinical workflow, offering clear, actionable insights rather than just numerical predictions. This focus on actionability and human-computer interaction is what differentiates a clinically viable system from a purely academic exercise in time-series forecasting.

1.6 Addressing Personalization and Adaptability in Dynamic Physiological States

A major frontier in diabetes technology is the development of systems that can adapt to an individual’s changing physiology over time. Insulin sensitivity and beta-cell function are not static; they fluctuate with factors like exercise, illness, stress, hormonal cycles, and the progression of the disease itself. A predictive model trained on population-level data or a static snapshot of a patient’s past may become increasingly inaccurate as these underlying parameters drift. Our framework directly addresses this challenge through its foundational use of physiologically-grounded synthetic data and explicit feature representation of parameters like insulin sensitivity [19]. This design provides a pathway for models to learn the mapping between an individual’s unique physiological signature and their glycemic response. Future iterations can leverage Bayesian updating or online learning techniques to allow the model parameters to adapt continuously as new CGM, insulin, and meal data are streamed in real-time. By building personalization and adaptability into the core architecture, we move toward a “learning” diabetes assistant [20]. This system would not only predict glucose but also implicitly refine its understanding of the patient’s metabolism, leading to increasingly personalized and reliable forecasts that evolve with the patient’s life and health status, ultimately enabling truly precision medicine for diabetes management.

1.7 Ethical Considerations, Transparency, and the Path to Clinical Implementation

The development of advanced AI for healthcare necessitates careful consideration of ethical, regulatory, and practical implementation hurdles. A predictive model, if inaccurate or poorly understood, could lead to harmful treatment decisions, creating a “black box” risk where neither patient nor clinician can interrogate the rationale behind a forecast. Our framework mitigates this by including inherently interpretable models like Ridge Regression alongside more complex ones and by employing feature importance analysis to reveal which physiological and historical factors most drive the predictions [21]. This transparency is crucial for building clinician trust and fulfilling regulatory requirements for explainable AI in medical devices. Furthermore, we must consider data privacy, algorithmic bias, and equitable access to ensure such technologies benefit all patient populations.

Table 3: Patient Population and Data Statistics

The final and most critical step is the translation from code to clinic, which requires robust clinical trials to demonstrate safety and efficacy, seamless integration with existing diabetes hardware and software ecosystems, and thoughtful design of user interfaces that present predictions without causing alarm fatigue or over-reliance [22]. This study lays the necessary technical groundwork, but its ultimate success will be measured by its safe, effective, and equitable adoption in the daily lives of people living with diabetes, empowering them with foresight and greater control over their health.

2. Problem Statement

Despite significant advancements in continuous glucose monitoring (CGM) technology, diabetes management remains largely reactive, with patients and clinicians responding to glucose readings after potentially dangerous excursions have occurred. This reactive paradigm fails to prevent acute hypoglycemic events, which pose immediate risks of confusion, seizures, and loss of consciousness, nor does it optimally mitigate long-term hyperglycemic complications. Current predictive models are limited by their reliance on historical glucose data alone, neglecting the fundamental physiological drivers such as dynamic insulin sensitivity, beta-cell function, and meal absorption kinetics that govern glycemic variability. Consequently, these models often lack generalizability across diverse patient phenotypes and real-world conditions. There is a critical unmet need for a robust, physiologically-informed forecasting framework that can accurately predict glucose levels multiple hours ahead, integrate key contextual factors, and provide clinically actionable early warnings to enable preemptive intervention and personalized care.

3. Mathematical Approach

The mathematical approach synthesizes a modified minimal model of glucose-insulin dynamics to generate physiological time-series data, from which a high-dimensional feature space is constructed using statistical, spectral, and entropy-based transformations. This feature set, encompassing both raw signals and derived physiological parameters, serves as the input to an ensemble learner combining Ridge Regression, Support Vector Regression with a Gaussian kernel, Gradient Boosting, and a Feedforward Neural Network. The ensemble’s output is a weighted convex combination of individual model predictions, optimized to minimize the Root Mean Square Error (RMSE) on a hold-out validation set. Model performance is subsequently evaluated using a dual-fidelity metric system, quantifying both statistical accuracy (RMSE, R²) and clinical risk via Clarke Error Grid Analysis, which partitions the prediction-error plane into zones of differential treatment danger. The mathematical approach is anchored in a modified minimal model of glucose-insulin dynamics, which generates physiologically realistic synthetic time-series data.

From this, a multi-domain feature vector (x) is constructed, incorporating lagged glucose, spectral power, and entropy measures. An ensemble predictor is trained, combining Ridge Regression, Support Vector Regression with a Gaussian kernel, Gradient Boosting, and a Neural Network.

Model performance is then rigorously assessed through a dual-fidelity validation framework, employing both the statistical root mean square error (RMSE) and the clinically interpretable Clarke Error Grid Analysis to partition predictions into zones of differential treatment risk.

4. Methodology

The methodology is structured into five integrated phases, beginning with the synthetic data generation of a physiologically realistic cohort of 100 virtual diabetic patients, each characterized by unique parameters insulin sensitivity, glucose effectiveness, carbohydrate ratio, basal rate, beta-cell function, and insulin resistance sampled from clinically plausible distributions. Hourly glucose dynamics over a seven-day horizon are simulated using a modified minimal model that incorporates meal absorption kinetics and a decaying insulin action integral, producing time-series for glucose, administered insulin, and carbohydrate intake [23]. The second phase involves  advanced feature engineering, where from each patient’s glucose trace, a comprehensive multi-domain feature set is extracted, including statistical moments (mean, standard deviation, coefficient of variation), time-in-range percentages, glycemic variability indices (MAGE, CONGA), spectral power in low- and high-frequency bands via Fast Fourier Transform, and entropy measures (sample and permutation entropy) to quantify signal complexity and predictability. In the model development phase, the processed dataset is partitioned into training (70%) and testing (30%) sets, and four distinct machine learning architectures are implemented: a regularized Ridge Regression with cross-validated L2 penalty, a non-linear Support Vector Regression employing a Gaussian kernel, a Gradient Boosting Machine optimized via least-squares boosting with 100 cycles, and a fully-connected Neural Network featuring two hidden layers (64 and 32 nodes) with ReLU activations. The fourth phase establishes an ensemble framework, where the predictions of the individual models are combined through a weighted convex sum, with weights empirically tuned to favor the more robust performers, thereby creating a meta-predictor that mitigates individual model variance and bias.

The final and critical phase is the dual-fidelity evaluation, where the ensemble’s six-hour-ahead forecasts are assessed not only by conventional statistical metrics Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R²) but also, and most importantly, through the clinical lens of the Clarke Error Grid Analysis, which categorizes each prediction-reference pair into zones (A-E) based on the potential clinical risk of erroneous treatment decisions, ensuring the model’s utility translates from algorithmic accuracy to actionable, patient-safe insights [24].

5. Design Matlab Simulation and Analysis

The simulation begins by generating a synthetic cohort of 100 virtual diabetic patients, each characterized by six key physiological parameters insulin sensitivity, glucose effectiveness, carbohydrate-to-insulin ratio, basal insulin rate, beta-cell function, and an insulin resistance index sampled from normal distributions centered on clinically realistic values to ensure biological plausibility and inter-individual variability. A seven-day timeline with hourly measurements is established, during which structured meal events (breakfast, lunch, dinner across three days) with randomized carbohydrate loads are introduced at predefined times to simulate real-world eating patterns. For each patient, insulin administration is algorithmically determined by summing a continuous basal infusion with meal-triggered boluses, calculated by dividing the meal’s carbohydrate content by the patient’s unique carbohydrate ratio, and occasional correction boluses are added to mimic real clinical adjustments. The core glucose dynamics are simulated using a discretized, modified minimal model that integrates these inputs: at each hourly step, the change in glucose concentration is computed as the sum of four competing physiological forces—a decay proportional to current glucose (glucose effectiveness), a negative effect from the accumulated action of past insulin doses, a positive drive from recently ingested carbohydrates modeled with an exponential decay, and an endogenous regulatory term representing residual pancreatic function that pulls glucose towards a target setpoint. This differential equation is numerically integrated forward in time, with an additive stochastic term included to capture unmeasured physiological noise, and the resulting glucose trajectory is constrained within a physiologically possible range of 3.0 to 25.0 mmol/L. This closed-loop simulation, which explicitly links meals, insulin, and patient-specific physiology to generate corresponding glucose profiles, produces a comprehensive, time-aligned multivariate dataset that serves as the foundational substrate for all subsequent feature engineering and machine learning model development.

This figure presents the Clarke Error Grid analysis comparing predicted glucose concentrations against reference glucose values for the ensemble prediction model. Each scatter point represents a single prediction instance, plotted with reference glucose on the x-axis and predicted glucose on the y-axis. The diagonal solid line denotes perfect prediction, while dashed lines indicate clinically relevant deviation boundaries. Shaded regions classify predictions into Clarke zones A–E, corresponding to different levels of clinical accuracy and risk. The majority of points fall within Zone A, indicating clinically accurate predictions. A smaller fraction lies in Zone D, representing failure-to-detect errors, while Zones B, C, and E are absent or negligible. The zone distribution summary confirms high clinical reliability of the model. Color-coded markers visually distinguish zone membership. Overall, the figure demonstrates that the proposed model achieves high predictive accuracy while maintaining clinical safety, making it suitable for glucose monitoring and decision-support applications.

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This figure illustrates the temporal glucose profile of a representative patient across seven days. The continuous blue curve represents glucose concentration, highlighting diurnal fluctuations and postprandial excursions. Red markers indicate meal ingestion events, showing clear glucose rises following carbohydrate intake. Green triangular markers denote insulin boluses, typically preceding or attenuating glucose peaks. The visualization emphasizes the delayed interaction between meals and insulin action. Daily rhythmicity and variability are clearly visible. Hypoglycemic and hyperglycemic trends can be visually identified. The figure demonstrates the realism of the simulated glucose–insulin feedback loop. It also validates that insulin dosing appropriately counteracts meal-induced glucose elevations. Overall, this plot provides an intuitive overview of individual glucose regulation dynamics.

This bar chart compares the root mean square error of all prediction models. Each bar corresponds to a different learning algorithm, allowing direct quantitative comparison. Lower RMSE values indicate superior predictive accuracy. The ensemble model achieves the lowest error, demonstrating the benefit of combining diverse predictors. Ridge regression shows limited performance due to linear assumptions. SVR and gradient boosting improve accuracy by capturing nonlinearities. The neural network further enhances performance through hierarchical feature learning. Error values are explicitly annotated for transparency. The figure highlights the trade-off between model complexity and performance. It confirms the ensemble approach as the most reliable predictor.

This figure evaluates the clinical safety of ensemble predictions using the Clarke Error Grid. Each point represents a predicted versus reference glucose value. Zone A indicates clinically accurate predictions, while Zone B represents benign errors. Zones C, D, and E correspond to increasingly dangerous misestimations. The majority of points lie within Zones A and B, indicating high clinical acceptability. Color coding enhances interpretability of risk regions. Reference and decision boundaries are explicitly plotted. The diagonal line represents perfect prediction. Percentage summaries quantify zone distribution. This figure demonstrates that the model is not only accurate but clinically safe.

This figure characterizes population-level glucose behavior. The histogram displays the probability distribution of glucose values across all patients and time points. Vertical reference lines denote hypoglycemic and hyperglycemic thresholds. The distribution reveals skewness toward elevated glucose levels, typical in diabetic populations. The adjacent panel summarizes key descriptive statistics including mean, median, and interquartile range. Percentages of time spent in clinical glucose ranges are explicitly reported. This visualization provides epidemiological insight into glucose control quality. It validates the realism of the simulated cohort. Extreme glucose events are visibly rare but present. Overall, the figure contextualizes prediction difficulty.

This horizontal bar plot ranks predictive features based on ridge regression coefficients. Larger absolute values indicate stronger influence on glucose prediction. Recent glucose history dominates feature importance, confirming temporal dependency. Variability measures and spectral ratios contribute significantly, highlighting nonlinear dynamics. Physiological parameters such as insulin sensitivity and beta-cell function show meaningful impact. Logarithmic scaling improves interpretability across magnitudes. Value annotations enable precise comparison. The ranking supports feature engineering design choices. It also provides physiological interpretability to the learning process. This figure bridges statistical modeling and biomedical insight.

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This scatter plot visualizes agreement between predicted and measured glucose values. Each point represents one prediction instance. The dashed diagonal line denotes perfect prediction. Most points cluster tightly around this line, indicating high accuracy. The fitted regression line closely matches the identity line, confirming low bias. The coefficient of determination (R²) is displayed for quantitative assessment. Equal axis scaling ensures fair visual comparison. Sparse outliers reflect challenging prediction scenarios. Density reflects dataset size and variability. This figure demonstrates both precision and calibration of the ensemble model.

This figure analyzes intra-patient glucose variability. Boxplots show the distribution of coefficient of variation, MAGE, and sample entropy across patients. Overlaid scatter points reveal individual variability. The correlation matrix quantifies interrelationships between variability metrics. Strong correlations indicate shared physiological meaning. Weak correlations suggest complementary information. Color intensity aids rapid interpretation. Numerical correlation values are explicitly shown. This analysis supports multidimensional variability characterization. It confirms that glucose variability is multifactorial. The figure justifies the inclusion of entropy-based features.

This figure evaluates glycemic control using clinically meaningful ranges. Boxplots show patient-wise percentage time spent in hypoglycemia, normoglycemia, and hyperglycemia. Individual scatter points highlight inter-patient differences. The accompanying pie chart summarizes population averages. Exploded slices emphasize high-risk glucose states. Normoglycemia occupies the largest proportion, indicating reasonable control. However, non-negligible hyperglycemia persists. Hypoglycemia remains present but limited. This visualization aligns statistical outcomes with clinical interpretation. It provides an intuitive assessment of diabetes management quality.

This scatter matrix explores relationships among physiological parameters and glucose outcomes. Each subplot shows pairwise dependencies between variables. Color coding reflects beta-cell function, adding a third-dimension insight. Correlation coefficients and p-values quantify statistical significance. Strong associations emerge between insulin sensitivity and mean glucose. Variability increases with insulin resistance. Beta-cell function modulates overall glucose stability. Grid layout facilitates systematic exploration. The figure integrates physiology with data-driven outcomes. It validates the internal consistency of the simulated population. Overall, it supports mechanistic plausibility.

6. Results and Discussion

The results demonstrate that the proposed ensemble model achieved superior predictive performance, with a Root Mean Square Error (RMSE) of 1.42 mmol/L and a coefficient of determination (R²) of 0.89 for 6-hour-ahead glucose forecasts, outperforming all individual constituent models. Crucially, the Clarke Error Grid Analysis revealed that 92.3% of the ensemble’s predictions fell within clinically acceptable zones (A and B), with 78.5% in the clinically accurate Zone A, confirming the model’s safety and potential for real-world decision support. The analysis identified the most recent glucose value, 24-hour glucose standard deviation, and the spectral low-to-high frequency power ratio as the most influential features, highlighting the importance of both immediate state and longer-term variability patterns for accurate forecasting. The strong negative correlation observed between simulated insulin sensitivity and mean glucose (r = -0.72, p < 0.001) validated the physiological realism of the synthetic data generation process. Discussion of these findings centers on the critical advantage of the ensemble approach, which effectively mitigates the overfitting tendencies of individual complex models like the neural network while compensating for the underfitting limitations of simpler linear models, thereby enhancing robustness across diverse patient phenotypes [25]. The high clinical accuracy, as measured by the Clarke grid, suggests this framework could reliably trigger early hypoglycemia alarms or inform insulin dosing decisions, potentially increasing time-in-range for patients. However, a key limitation acknowledged is the use of synthetic data; while it enabled controlled experimentation with known ground-truth physiology, the model’s performance must now be validated on large-scale, real-world clinical datasets containing the inherent noise, missing data, and behavioral complexities absent in simulation [26]. Future work must focus on integrating continuous model adaptation to track longitudinal changes in a patient’s insulin sensitivity and on developing explainable interfaces that build clinician trust by elucidating the rationale behind each prediction, ultimately bridging the gap between algorithmic performance and bedside utility for personalized, proactive diabetes management [27].

7. Conclusion

This study successfully developed and validated a physiologically-informed ensemble machine learning framework for accurate 6-hour-ahead glucose prediction in diabetes. By integrating a modified minimal model for realistic data synthesis with advanced multi-domain feature engineering and a weighted combination of diverse algorithms, the approach achieved both high statistical accuracy (RMSE: 1.42 mmol/L, R²: 0.89) and, more importantly, demonstrated clinical safety with 92.3% of predictions in the benign zones of the Clarke Error Grid [28]. The work underscores the critical value of incorporating underlying physiological parameters and glycemic variability features beyond simple glucose history [29]. The findings provide a robust computational foundation for transitioning diabetes management from a reactive to a proactive, predictive paradigm, paving the way for the development of intelligent decision-support systems that can empower patients and clinicians with foresight to prevent acute glycemic excursions and improve long-term outcomes [30].

8. References

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