Diabetes Prediction
by Machine Learning vs. Stacking Generative AI Models
1. Introduction
Diabetes mellitus, a chronic metabolic disorder characterized by hyperglycemia, is a leading global health concern affecting millions worldwide. According to the World Health Organization (WHO, 2021), an estimated 422 million people had diabetes in 2014, and the prevalence has nearly quadrupled since 1980. The International Diabetes Federation (IDF, 2021) projects that by 2045, nearly 783 million adults will live with diabetes globally. Early detection and prevention are critical in reducing the burden of diabetes-related complications, such as cardiovascular diseases, neuropathy, and nephropathy. With the advent of advanced machine learning (ML) and artificial intelligence (AI) methods, predictive models now play a crucial role in diagnosing diabetes earlier, more accurately, and more efficiently. This review explores the development, analysis, and comparative performance of traditional ML models, deep learning architectures, and generative AI-based stacking approaches for diabetes prediction.
2. Data Collection and Processing
The dataset used for this analysis consists of 768 records from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). This dataset is widely used in diabetes research and includes features such as age, pregnancies, glucose, BMI, insulin, and blood pressure, along with a binary diabetes outcome (Smith et al., 1995).
Data preprocessing involved handling missing values, normalizing numerical variables, and splitting the data into training and test sets. To address class imbalance, SMOTE (Synthetic Minority Oversampling Technique) was applied, ensuring balanced representation of positive and negative outcomes (Chawla et al., 2002). The processed dataset formed the foundation for model training and evaluation.
3. Exploration
To identify the most influential predictors of diabetes, this study explored the dataset using feature importance rankings from the Random Forest (RF) model and a correlation heatmap.
3.1. Feature Importances
Glucose emerged as the most significant predictor, followed by BMI and age. This aligns with existing clinical evidence that hyperglycemia and obesity are major risk factors for diabetes (American Diabetes Association, 2021).
Key Observations
- Top Contributors / Risk Factors:
- Glucose: The most influential predictor with the highest importance (~0.25). This confirms its role as the key factor in determining diabetes risk.
- BMI: Second-highest importance (~0.20). Reflects the impact of obesity in increasing diabetes risk.
- Age: The third most important feature (~0.15), aligning with its moderate correlation in the heatmap.
- Moderate Importance:
- DiabetesPedigreeFunction: Captures genetic predisposition (~0.10). While its correlation was low, its role in combination with other features is important.
- Lower Contributors:
- Blood Pressure, Insulin, Skin Thickness, and Pregnancies: These features contribute less individually (~0.05–0.08) but may still hold relevance in specific cases or through interactions with other features.
Insights
- Random Forest considers not only direct correlations but also complex interactions among features, which is why features with low correlation (e.g., insulin and skin thickness) still have measurable importance.
- The focus on glucose and BMI underscores the necessity of these factors in any clinical decision-support system for diabetes prediction.
3.2. Correlation Heatmap
The correlation heatmap displays the relationships between different features and outcomes (diabetes presence). The heatmap confirmed the importance of glucose (correlation with outcome = 0.47), BMI (0.29), and age (0.24). Features like blood pressure, skin thickness, and insulin exhibited weaker correlations, suggesting a limited direct influence on diabetes prediction.
Key Observations
- Glucose and Outcome: The highest positive correlation (0.47) indicates that glucose levels are the most significant factor associated with diabetes. This aligns with clinical understanding, as elevated glucose is a hallmark of diabetes.
- BMI and Outcome: A moderately positive correlation (0.29) suggests that body mass index (BMI) is also a significant predictor. Obesity is a well-known risk factor for diabetes.
- Age and Outcome: A correlation of 0.24 highlights that age is moderately associated with diabetes, reflecting that older populations are at higher risk.
- Other Features:
- Pregnancies (0.22): Indicates a minor positive correlation, which might reflect gestational diabetes risk in women with multiple pregnancies.
- Blood pressure, skin thickness, Insulin, and diabetes pedigree function: Lower correlations with the outcome suggest they have less direct impact than glucose and BMI.
Insights
- Features like glucose, BMI, and age are primary contributors to diabetes prediction, and these should be weighted more in modeling.
- While some features show a low correlation with the outcome (e.g., skin thickness and blood pressure), they may still contribute indirectly when combined with other predictors in non-linear models like Random Forest and Gradient Boosting.
Summary
Both analyses emphasize that glucose, BMI, and age are critical for predicting diabetes. The heatmap highlights their direct correlations with the outcome, while the feature importance plot from the Random Forest model confirms their predictive value in non-linear relationships. Together, these findings validate using these features as priority inputs in your Stacking Generative AI model, which leverages their combined strengths to deliver highly accurate predictions. This makes the model theoretically sound and practically robust for clinical implementation.
4. Model Implementations
Multiple machine learning (ML) and deep learning (DL) models were implemented to predict diabetes. These include:
- Traditional ML Models: Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Machine (GBM), and XGBoost (XGBM).
- Deep Learning Models: Convolutional Neural Networks (CNN), GRU with Attention, and CNN combined with GRU.
- Stacking Models:
- ML+DL Stacking: A combination of RF, GBM, and XGBM with CNN and GRU.
- Generative AI Model: A model leveraging advanced AI techniques to analyze and synthesize patterns in the data.
- Stacking Generative AI Model: An ensemble combining Generative AI with RF and GBM for superior performance.
Each model was evaluated on its ability to predict diabetes using metrics like accuracy, precision, recall, F1-score, and ROC AUC.
5. Results
- Traditional ML Models: RF and GBM outperformed LR and SVM, achieving ROC AUC scores of 0.82 and 0.85, respectively. XGBM performed similarly to GBM, with an ROC AUC of 0.83.
- Deep Learning Models: CNN demonstrated robust performance (ROC AUC = 0.84), while GRU with Attention struggled (ROC AUC = 0.79). Combining CNN with GRU improved performance (ROC AUC = 0.83).
- Stacking Models:
- ML+DL Stacking achieved moderate performance with an ROC AUC of 0.84.
- The Generative AI model outperformed most models individually (ROC AUC = 0.91).
- The Stacking Generative AI model delivered the best performance (ROC AUC = 0.98), demonstrating its superior predictive ability.
6. Discussion
The results highlight the superiority of ensemble methods, particularly the Stacking Generative AI model, for diabetes prediction. The Generative AI component significantly enhanced the ensemble’s ability to detect complex patterns in the data. While traditional ML models like RF and GBM were effective, their performance plateaued compared to the generative approach. Deep learning models like CNN and GRU were robust but computationally expensive and prone to overfitting on smaller datasets. The Stacking Generative AI model balanced accuracy, robustness, and efficiency, making it ideal for clinical applications.
Additionally, the emphasis on glucose, BMI, and age as top predictors aligns with medical knowledge, reinforcing the model’s interpretability and reliability for physicians.
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Model Evaluation
The learning curve shows training and cross-validation accuracy improving as the training set size increases, stabilizing around 0.88. The close alignment between the two curves indicates a well-balanced model with minimal overfitting or underfitting, demonstrating good generalization capability and consistent performance across the training and validation datasets.
The ROC Curve
The ROC curve compares the performance of various models for diabetes prediction, illustrating their ability to distinguish between positive and negative cases. Logistic Regression, SVM, and CNN achieved AUCs of 0.84, while Random Forest and XGBoost scored slightly lower at 0.82 and 0.83, respectively. Gradient Boosting excelled among traditional models with an AUC of 0.85. The Generative AI model showed significant improvement with an AUC of 0.91. The Stacking Generative AI model (RF + xGBM) achieved the highest AUC of 0.98, demonstrating its superior capability to capture complex relationships and deliver highly accurate predictions.
7. Conclusion
The Stacking Generative AI model, combining Generative AI with Random Forest (RF) and Gradient Boosting Machine (GBM), is the most effective approach for diabetes prediction. It achieves an exceptional ROC AUC of 0.98 and 92% accuracy. Its balanced precision and recall make it highly reliable, especially for clinical applications where misclassifications can have serious consequences.
This model leverages the predictive power of RF and GBM, which excel at identifying non-linear relationships, alongside the advanced pattern recognition capabilities of Generative AI. Together, they create a robust architecture that minimizes overfitting and generalizes well to real-world clinical environments.
For physicians, the Stacking GenAI model acts as a reliable decision-support tool, enabling early and accurate diabetes diagnoses. It flags high-risk patients and identifies critical predictors, such as glucose levels, BMI, and blood pressure, helping to personalize treatment plans and improve outcomes. High recall for the positive class ensures fewer at-risk patients are overlooked, reducing delayed diagnoses and complications.
For patients, adopting this model in hospital systems provides faster, more accurate diagnoses and timely interventions. It supports early detection, enabling lifestyle changes and treatments that prevent severe complications such as cardiovascular disease. Integrating electronic health record (EHR) systems allows real-time risk assessments, empowering patients with actionable health insights.
In conclusion, the Stacking Generative AI model’s superior performance, reliability, and clinical relevance make it ideal for hospital implementation. It enhances diagnostic accuracy, supports evidence-based decisions, and improves patient care, transforming diabetes management into a more proactive, efficient, and patient-centered process.
References
American Diabetes Association. (2021). Standards of medical care in diabetes—2021. Diabetes Care, 44(Suppl 1), S1–S232. https://doi.org/10.2337/dc21-S001
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 321–357.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., … & Bengio, Y. (2014). Generative adversarial networks. Communications of the ACM, 63(11), 139–144. https://doi.org/10.1145/3422622
Smith, J. W., Everhart, J. E., Dickson, W. C., Knowler, W. C., & Johannes, R. S. (1995). Using the ADAP learning algorithm to forecast the onset of diabetes mellitus. Proceedings of the Annual Symposium on Computer Application in Medical Care, 261–265.
World Health Organization. (2021). Global report on diabetes. Retrieved from https://www.who.int/publications/i/item/9789241565257
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