Clinical Decision Support Systems for Dementia Management Using Predictive Analytics and Explainable AI

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Moses Adeolu Agoi
https://orcid.org/0000-0002-8910-2876
Gbenga Oyewole Ogunsanwo
Bamidele Olumuyiwa Alaba
Asep Surya Maulana

Abstract

Research Background: Dementia is a growing global public health challenge driven by population ageing and increased life expectancy. Clinical Decision Support Systems (CDSS) have emerged as important tools to assist clinicians in early diagnosis, risk stratification, prognosis estimation, and personalized care planning in dementia management. Recent advances in predictive analytics and artificial intelligence (AI), particularly machine learning and deep learning models, have significantly enhanced the analytical capabilities of CDSS. However, the integration of these technologies into clinical practice remains limited due to concerns related to interpretability, generalizability, and ethical accountability. This study aims to review the development of CDSS for dementia management that integrate predictive analytics with Explainable Artificial Intelligence (XAI).


Methods: A systematic literature review was conducted using peer-reviewed publications from major academic databases published between 2017 and 2025. The analysis focuses on algorithmic approaches, data sources, validation strategies, and explainability techniques applied in contemporary dementia CDSS.


Key Findings: The findings indicate that predictive models demonstrate high accuracy in detecting early cognitive impairment and predicting disease progression. Nevertheless, their clinical implementation is often constrained by the “black-box” nature of many AI models and limited external validation. Explainable AI methods such as SHAP, LIME, and attention-based networks are increasingly used to improve transparency and clinician trust.


Contribution: This study contributes an integrative perspective that emphasizes the importance of balancing predictive performance with interpretability, ethical governance, and clinical usability.


Conclusion: It concludes that integrating predictive analytics with XAI is essential for developing trustworthy and clinically applicable CDSS in dementia care.

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References

Arbabshirani, Mohammad R., Sergey Plis, Jing Sui, dan Vince D. Calhoun. 2017. “Single Subject Prediction of Brain Disorders in Neuroimaging: Promises and Pitfalls.” NeuroImage 145:137–65. doi:10.1016/j.neuroimage.2016.02.079.

Bhandarkar, Anish, Pratham Naik, Kavita Vakkund, Srasthi Junjappanavar, Savita Bakare, dan Santosh Pattar. 2024. “Deep Learning Based Computer Aided Diagnosis of Alzheimer’s Disease: A Snapshot of Last 5 Years, Gaps, and Future Directions.” Artificial Intelligence Review 57(2):30. doi:10.1007/s10462-023-10644-8.

Choi, Edward, Mohammad Taha Bahadori, Jimeng Sun, Joshua Kulas, Andy Schuetz, dan Walter Stewart. 2016. “RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism.” dalam Advances in Neural Information Processing Systems. Vol. 29. Curran Associates, Inc.

Clinical Decision Support (CDS) | Digital Healthcare Research. t.t. Diambil 10 Maret 2026. https://digital.ahrq.gov/health-it-tools-and-resources/clinical-decision-support-cds.

Holzinger, Andreas, Georg Langs, Helmut Denk, Kurt Zatloukal, dan Heimo Müller. 2019. “Causability and Explainability of Artificial Intelligence in Medicine.” WIREs Data Mining and Knowledge Discovery 9(4):e1312. doi:10.1002/widm.1312.

International, Alzheimer’s Disease. 2023. “World Alzheimer Report 2023: Reducing Dementia Risk: Never Too Early, Never Too Late.” https://www.alzint.org/resource/world-alzheimer-report-2023/.

Jo, Taeho, Kwangsik Nho, dan Andrew J. Saykin. 2019. “Deep Learning in Alzheimer’s Disease: Diagnostic Classification and Prognostic Prediction Using Neuroimaging Data.” Frontiers in Aging Neuroscience 11. doi:10.3389/fnagi.2019.00220.

Lundberg, Scott, dan Su-In Lee. 2017. “A Unified Approach to Interpreting Model Predictions.”

Mehrabi, Ninareh, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, dan Aram Galstyan. 2022. “A Survey on Bias and Fairness in Machine Learning.” ACM Computing Surveys 54(6):1–35. doi:10.1145/3457607.

Mohsen, Saeed. 2025. “Alzheimer’s Disease Detection Using Deep Learning and Machine Learning: A Review.” Artificial Intelligence Review 58(9):262. doi:10.1007/s10462-025-11258-y.

Nguyen, Minh, Tong He, Lijun An, Daniel C. Alexander, Jiashi Feng, dan B. T. Thomas Yeo. 2020. “Predicting Alzheimer’s Disease Progression Using Deep Recurrent Neural Networks.” NeuroImage 222:117203. doi:10.1016/j.neuroimage.2020.117203.

Sendak, Mark P., William Ratliff, Dina Sarro, Elizabeth Alderton, Joseph Futoma, Michael Gao, Marshall Nichols, Mike Revoir, Faraz Yashar, Corinne Miller, Kelly Kester, Sahil Sandhu, Kristin Corey, Nathan Brajer, Christelle Tan, Anthony Lin, Tres Brown, Susan Engelbosch, Kevin Anstrom, Madeleine Clare Elish, Katherine Heller, Rebecca Donohoe, Jason Theiling, Eric Poon, Suresh Balu, Armando Bedoya, dan Cara O’Brien. 2020. “Real-World Integration of a Sepsis Deep Learning Technology Into Routine Clinical Care: Implementation Study.” JMIR Medical Informatics 8(7):e15182. doi:10.2196/15182.

Sutton, Reed T., David Pincock, Daniel C. Baumgart, Daniel C. Sadowski, Richard N. Fedorak, dan Karen I. Kroeker. 2020. “An Overview of Clinical Decision Support Systems: Benefits, Risks, and Strategies for Success.” Npj Digital Medicine 3(1):17. doi:10.1038/s41746-020-0221-y.

Tonekaboni, Sana, Shalmali Joshi, Melissa D. McCradden, dan Anna Goldenberg. 2019. “What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use.”

UNESCO. 2021. “Recommendation on the Ethics of Artificial Intelligence.”

Wang, Tingyan, Robin G. Qiu, dan Ming Yu. 2018. “Predictive Modeling of the Progression of Alzheimer’s Disease with Recurrent Neural Networks.” Scientific Reports 8(1):9161. doi:10.1038/s41598-018-27337-w.

World Health Organization. 2023. “Dementia: Key Facts.”

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