Skip to main content

Explainable Artificial Intelligence in Medical Diagnostics: Insights into Alzheimer’s Disease

  • Conference paper
  • First Online:
Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2136))

  • 7 Accesses

Abstract

Alzheimer’s Disease (AD) is the most prevalent form of dementia globally, which presents a pressing health issue, especially in aging populations. Its early detection is critical to initiating appropriate care and therapeutic strategies. However, AD’s complex and multifaceted nature poses considerable challenges to accurate and early diagnosis. Machine learning (ML) models have emerged as promising disease detection and diagnosis tools, including AD. However, despite their superior predictive performance, these models are often viewed as “black boxes” due to their complex internal workings, which are not readily interpretable. This study aims to explore the application of Explainable Artificial Intelligence (XAI) techniques to enhance the interpretability of the best-performing ML classifier for AD detection. The robust analysis offers significant insights into the ML model’s decision-making processes, thereby enhancing their interpretability and bolstering confidence in their use for early AD detection.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Yang, P., Sun, F.: Aducanumab: the first targeted Alzheimer’s therapy. Drug Discoveries Ther. 15(3), 166–168 (2021)

    Article  MATH  Google Scholar 

  2. Meng, W., et al.: Female perspective: the burden of Alzheimer’s disease and other dementias in china from: to 2019 and prediction of their prevalence up to 2044. Front. Public Health 11, 2023 (1990)

    Google Scholar 

  3. Angelopoulou, E., et al.: How telemedicine can improve the quality of care for patients with Alzheimer’s disease and related dementias? A narrative review. Medicina 58(12), 1705 (2022)

    Google Scholar 

  4. Manemann, S.M., et al.: Alzheimer’s disease and related dementias and heart failure: a community study. J. Am. Geriatr. Soc. 70(6), 1664–1672 (2022)

    Google Scholar 

  5. Ahmad, Z., Rahim, S., Zubair, M., Abdul-Ghafar, J.: Artificial intelligence (AI) in medicine, current applications and future role with special emphasis on its potential and promise in pathology: present and future impact, obstacles including costs and acceptance among pathologists, practical and philosophical considerations. a comprehensive review. Diagn. Pathol. 16, 1–16 (2021)

    Google Scholar 

  6. Dashwood, M., Churchhouse, G., Young, M., Kuruvilla, T.: Artificial intelligence as an aid to diagnosing dementia: an overview. Prog. Neurol. Psychiatry 25(3), 42–47 (2021)

    Article  Google Scholar 

  7. Ali, S., et al.: Explainable artificial intelligence (XAI): what we know and what is left to attain trustworthy artificial intelligence. Inf. Fusion, 101805 (2023)

    Google Scholar 

  8. Gao, X.R., et al.: Explainable machine learning aggregates polygenic risk scores and electronic health records for Alzheimer’s disease prediction. Sci. Rep. 13(1), 450 (2023)

    Google Scholar 

  9. Ribeiro, M.T., Singh, S., Guestrin, C.: “Why should i trust you?” Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135–1144 (2016)

    Google Scholar 

  10. Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Advances in Neural Information Processing Systems, vol. 30 (2017)

    Google Scholar 

  11. Haohui, L., Uddin, S.: Explainable stacking-based model for predicting hospital readmission for diabetic patients. Information 13(9), 436 (2022)

    Article  MATH  Google Scholar 

  12. Basheer, S., Bhatia, S., Sakri, S.B.: Computational modeling of dementia prediction using deep neural network: analysis on oasis dataset. IEEE Access 9, 42449–42462 (2021)

    Google Scholar 

  13. Venugopalan, J., Tong, L., Hassanzadeh, H.R., Wang, M.D.: Multimodal deep learning models for early detection of Alzheimer’s disease stage. Sci. Rep. 11(1), 3254 (2021)

    Google Scholar 

  14. Raju, V.N.G., Prasanna Lakshmi, K., Jain, V.M., Kalidindi, A., Padma, V.: Study the influence of normalization/transformation process on the accuracy of supervised classification. In: 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT), pp. 729–735. IEEE (2020)

    Google Scholar 

  15. Matloff, N.: Statistical Regression and Classification: From Linear Models to Machine Learning. CRC Press (2017)

    Google Scholar 

  16. Clark, L.A., Pregibon, D.: Tree-based models. In: Statistical Models in S, pp. 377–419. Routledge (2017)

    Google Scholar 

  17. Ali, H.A., Mohamed, C., Abdelhamid, B., Ourdani, N., El Alami, T.: A comparative evaluation use bagging and boosting ensemble classifiers. In: 2022 International Conference on Intelligent Systems and Computer Vision (ISCV), pp. 1–6. IEEE (2022)

    Google Scholar 

  18. Murphy, K.P.: Machine Learning: A Probabilistic Perspective. MIT Press (2012)

    Google Scholar 

  19. Kumar, M.: Using machine learning to predict heart-related diseases. IUP J. Comput. Sci. 16(3), 22–34 (2022)

    MathSciNet  MATH  Google Scholar 

  20. Bentéjac, C., Csörgő, A., Martínez-Muñoz, G.: A comparative analysis of gradient boosting algorithms. Artif. Intell. Rev. 54, 1937–1967 (2021)

    Article  MATH  Google Scholar 

  21. Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press (2016)

    Google Scholar 

  22. Tharwat, A.: Linear vs. quadratic discriminant analysis classifier: a tutorial. Int. J. Appl. Pattern Recogn. 3(2), 145–180 (2016)

    Google Scholar 

  23. Sokolova, M., Japkowicz, N., Szpakowicz, S.: Beyond accuracy, F-score and ROC: a family of discriminant measures for performance evaluation. In: Sattar, A., Kang, B. (eds.) AI 2006. LNCS (LNAI), vol. 4304, pp. 1015–1021. Springer, Heidelberg (2006). https://doi.org/10.1007/11941439_114

    Chapter  MATH  Google Scholar 

  24. Miotto, R., Wang, F., Wang, S., Jiang, X., Dudley, J.T.: Deep learning for healthcare: review, opportunities and challenges. Briefings Bioinf. 19(6), 1236–1246 (2018)

    Google Scholar 

  25. Islam, J., Zhang, Y.: A novel deep learning based multi-class classification method for Alzheimer’s disease detection using brain MRI data. In: Zeng, Y., et al. (eds.) BI 2017. LNCS (LNAI), vol. 10654, pp. 213–222. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-70772-3_20

    Chapter  Google Scholar 

Download references

Acknowledgment

The UAEU supported this research under grant number 12R000.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Amir Ahmad .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Nawaz, A., Ahmad, A. (2025). Explainable Artificial Intelligence in Medical Diagnostics: Insights into Alzheimer’s Disease. In: Meo, R., Silvestri, F. (eds) Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2023. Communications in Computer and Information Science, vol 2136. Springer, Cham. https://doi.org/10.1007/978-3-031-74640-6_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-74640-6_23

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-74639-0

  • Online ISBN: 978-3-031-74640-6

  • eBook Packages: Artificial Intelligence (R0)

Publish with us

Policies and ethics