TY - GEN
T1 - Explainable Artificial Intelligence in Medical Diagnostics
T2 - Joint European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2023
AU - Nawaz, Ali
AU - Ahmad, Amir
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025
Y1 - 2025
N2 - 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.
AB - 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.
KW - Aging Problem
KW - Alzheimer Disease
KW - Explainable Artificial Intelligence
KW - Machine learning
UR - https://www.scopus.com/pages/publications/85215985954
UR - https://www.scopus.com/pages/publications/85215985954#tab=citedBy
U2 - 10.1007/978-3-031-74640-6_23
DO - 10.1007/978-3-031-74640-6_23
M3 - Conference contribution
AN - SCOPUS:85215985954
SN - 9783031746390
T3 - Communications in Computer and Information Science
SP - 312
EP - 319
BT - Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2023, Revised Selected Papers
A2 - Meo, Rosa
A2 - Silvestri, Fabrizio
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 18 September 2023 through 22 September 2023
ER -