Alzheimer's Disease Surveillance Using CDC Data and Hybrid Ensemble Models
DOI:
https://doi.org/10.56532/mjsat.v6i4.746Keywords:
Healthy aging data, Epidemiological forecasting, Gradient boosting machines, Deep learning, Data leakage preventionAbstract
Alzheimer’s disease (AD) is a problem that will be very hard to deal with globally in public health, but most AI (Artificial Intelligence) solutions for AD prediction are still based on neuroimaging methods (e.g., MRI and PET) that are very expensive. This restricts the use of AI for monitoring at the population level. Hence, there is a need for predictive frameworks that can rely on public health data, which are available in tabular format, to forecast the trends of diseases like AD affecting the population. In this research, we put forward a Dual-Stream Hybrid Ensemble Framework for the adoption of Alzheimer's disease predictive methods by longitudinal CDC data (2015–2022). The research works on checking the ability of modern deep learning architectures to outperform the strong Gradient Boosting Machine baselines on complex epidemiological tabular datasets. The framework consists of a Feature Tokenizer Transformer (FT-Transformer) joined with three leading GBMs (XGBoost, LightGBM, CatBoost) and a deep Multi-Layer Perceptron (MLP). To maintain the validity of the scientific findings, we impose a strict “Chinese Wall” feature segregation rule to avoid data leakage by disabling the use of context-specific proxy identifiers in the binary classification stream. Furthermore, Probability Target Encoding and Cost-Sensitive Learning methods are put into practice to tackle high-cardinality categorical features and severe class imbalance issues (~1:9.5). The outcomes of the experiments indicate that the FT-Transformer provides the best regression performance with an R-squared value of 0.9451, while XGBoost gives a lower value of 0.8752, and the final weighted regression ensemble provides an R-squared value of 0.9394. For classification under leakage constraints, the cost-sensitive ensemble reaches high sensitivity at the cost of precision at Recall = 89.75%, thereby indicating a demonstrates utility for identifying AD/SCD-related surveillance strata even though metadata proxies have been removed. To sum it up, the new research results give empirical evidence that deep learning by transformers can outperform classic boosting methods for public health monitoring of Alzheimer’s disease if the right tokenization and training restrictions are applied.
References
World Health Organization, "Dementia," WHO Health Topics, 2025. url: https://www.who.int/health-topics/dementia
Alzheimer's Association, "2024 Alzheimer's disease facts and figures," Alzheimer's & Dementia, 2024. doi: https://doi.org/10.1002/alz.13809
Alzheimer's Disease International, World Alzheimer report 2024: Global changes in attitudes to dementia. London, UK: ADI, 2024.
G. Livingston et al., "Dementia prevention, intervention, and care: 2020 report of the Lancet Commission," The Lancet, vol. 396, no. 10248, pp. 413–446, 2020. doi: https://doi.org/10.1016/S0140-6736(20)30367-6
Centers for Disease Control and Prevention, "Alzheimer's disease and healthy aging data portal," CDC, 2024. url: https://data.cdc.gov
C. A. Taylor, E. D. Bouldin, and L. C. McGuire, "Subjective cognitive decline among adults aged ≥45 years – United States, 2015–2016," MMWR Morbidity and Mortality Weekly Report, vol. 67, no. 27, pp. 753–757, 2018. doi: https://doi.org/10.15585/mmwr.mm6727a1
Centers for Disease Control and Prevention, "Behavioral risk factor surveillance system (BRFSS): Overview," CDC, 2024. url: https://www.cdc.gov/brfss
U.S. Department of Health & Human Services / CDC, "Alzheimer's disease and healthy aging data," dataset metadata record, Data Catalog, 2024. url: https://data.cdc.gov
S. Rajendran et al., "Hybrid machine learning model using particle swarm optimization for effectual diagnosis of Alzheimer's disease from handwriting," in Proc. 4th Int. Conf. Circuits, Control, Communication and Computing (I4C), 2022. doi: https://doi.org/10.1109/I4C57141.2022.10057948
A. Rajkomar et al., "Scalable and accurate deep learning with electronic health records," npj Digital Medicine, vol. 1, art. 18, 2018. doi: https://doi.org/10.1038/s41746-018-0029-1
R. Miotto, L. Li, B. A. Kidd, and J. T. Dudley, "Deep patient: An unsupervised representation to predict the future of patients from the electronic health records," Scientific Reports, vol. 6, art. 26094, 2016. doi: https://doi.org/10.1038/srep26094
Z. Obermeyer, B. Powers, C. Vogeli, and S. Mullainathan, "Dissecting racial bias in an algorithm used to manage the health of populations," Science, vol. 366, no. 6464, pp. 447–453, 2019. doi: https://doi.org/10.1126/science.aax2342
J. H. Friedman, "Greedy function approximation: A gradient boosting machine," The Annals of Statistics, vol. 29, no. 5, pp. 1189–1232, 2001. doi: https://doi.org/10.1214/aos/1013203451
T. Chen and C. Guestrin, "XGBoost: A scalable tree boosting system," in Proc. 22nd ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, 2016, pp. 785–794. doi: https://doi.org/10.1145/2939672.2939785
G. Ke et al., "LightGBM: A highly efficient gradient boosting decision tree," in Advances in Neural Information Processing Systems (NeurIPS), vol. 30, 2017.
L. Prokhorenkova, G. Gusev, A. Vorobev, A. V. Dorogush, and A. Gulin, "CatBoost: Unbiased boosting with categorical features," in Advances in Neural Information Processing Systems (NeurIPS), vol. 31, 2018.
A. Vaswani et al., "Attention is all you need," in Advances in Neural Information Processing Systems (NeurIPS), vol. 30, 2017.
Y. Gorishniy, I. Rubachev, V. Khrulkov, and A. Babenko, "Revisiting deep learning models for tabular data," in Advances in Neural Information Processing Systems (NeurIPS), vol. 34, pp. 4224–4234, 2021.
A. Niculescu-Mizil and R. Caruana, "Predicting good probabilities with supervised learning," in Proc. 22nd Int. Conf. Machine Learning (ICML), 2005, pp. 625–632. doi: https://doi.org/10.1145/1102351.1102430
S. M. Lundberg and S.-I. Lee, "A unified approach to interpreting model predictions," in Advances in Neural Information Processing Systems (NeurIPS), vol. 30, 2017.
C. R. Jack Jr. et al., "NIA-AA research framework: Toward a biological definition of Alzheimer's disease," Alzheimer's & Dementia, vol. 14, no. 4, pp. 535–562, 2018. doi: https://doi.org/10.1016/j.jalz.2018.02.018
A. M. Singh et al., "A systematic review on machine learning and deep learning techniques in the effective diagnosis of Alzheimer's disease," Brain Informatics, vol. 10, no. 1, 2023. doi: https://doi.org/10.1186/s40708-023-00195-7
S. A. Raji et al., "Artificial intelligence in Alzheimer's disease diagnosis and prognosis using PET-MRI: A narrative review of high-impact literature post-Tauvid approval," Journal of Clinical Medicine, vol. 14, no. 2, 2025. doi: https://doi.org/10.3390/jcm14165913
Centers for Disease Control and Prevention, "BRFSS cognitive decline module," CDC, 2024. url: https://www.cdc.gov/brfss
J. H. Park et al., "Machine learning prediction of incidence of Alzheimer's disease using large-scale administrative health data," npj Digital Medicine, vol. 3, no. 1, 2020. doi: https://doi.org/10.1038/s41746-020-0256-0
A. Etholén et al., "Forecasting subjective cognitive decline: AI approach using dynamic Bayesian networks," Journal of Medical Internet Research, vol. 27, 2025. doi: https://doi.org/10.2196/65028
T. G. Dietterich, "Ensemble methods in machine learning," in Proc. Multiple Classifier Systems, 2000, pp. 1–15. doi: https://doi.org/10.1007/3-540-45014-9_1
M. T. Ribeiro, S. Singh, and C. Guestrin, "'Why should I trust you?' Explaining the predictions of any classifier," in Proc. 22nd ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, 2016, pp. 1135–1144. doi: https://doi.org/10.1145/2939672.2939778
C. Guo, G. Pleiss, Y. Sun, and K. Q. Weinberger, "On calibration of modern neural networks," in Proc. 34th Int. Conf. Machine Learning (ICML), 2017, pp. 1321–1330.
S. Kaufman, S. Rosset, C. Perlich, and O. Stitelman, "Leakage in data mining: Formulation, detection, and avoidance," ACM Transactions on Knowledge Discovery from Data, vol. 6, no. 4, pp. 1–21, 2012. doi: https://doi.org/10.1145/2382577.2382579
N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, "SMOTE: Synthetic minority over-sampling technique," Journal of Artificial Intelligence Research, vol. 16, pp. 321–357, 2002. doi: https://doi.org/10.1613/jair.953
H. He and E. A. Garcia, "Learning from imbalanced data," IEEE Transactions on Knowledge and Data Engineering, vol. 21, no. 9, pp. 1263–1284, 2009. doi: https://doi.org/10.1109/TKDE.2008.239
Z. C. Lipton, "The mythos of model interpretability," ACM Queue, vol. 16, no. 3, pp. 31–57, 2018. doi: https://doi.org/10.1145/3236386.3241340
S. G. Finlayson et al., "The clinician and datasets: Preventing data leakage in medical AI," NEJM AI, vol. 1, no. 2, 2024.
R. Snead, L. Dumenci, and R. M. Jones, "A latent class analysis of cognitive decline in US adults, BRFSS 2015–2020," BMC Public Health, vol. 22, art. 1554, 2022. doi: https://doi.org/10.1186/s12889-022-14001-2
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Copyright (c) 2026 Mehedi Hasan, Asif Mahamud Shaon, MD Tanjirul Islam, Fatima Adon, Anik Das, S M Abdullah Shafi
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