Transparent insights into alzheimer’s progression: a time-aware approach with explainable
Alzheimer’s disease, acknowledged for its intricate and degenerative characteristics, presents considerable challenges, particularly among the elderly population. The relentless nature of Alzheimer’s, marked by the gradual deterioration of cognitive function, underscores the urgency to develop effec...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Penerbit Universiti Kebangsaan Malaysia
2024
|
| Online Access: | http://journalarticle.ukm.my/25583/ http://journalarticle.ukm.my/25583/1/kejut_34.pdf |
| _version_ | 1848816397032882176 |
|---|---|
| author | Abraham Varghese, Ben George, Vinu Sherimon, Prashanth Gouda, |
| author_facet | Abraham Varghese, Ben George, Vinu Sherimon, Prashanth Gouda, |
| author_sort | Abraham Varghese, |
| building | UKM Institutional Repository |
| collection | Online Access |
| description | Alzheimer’s disease, acknowledged for its intricate and degenerative characteristics, presents considerable challenges, particularly among the elderly population. The relentless nature of Alzheimer’s, marked by the gradual deterioration of cognitive function, underscores the urgency to develop effective strategies for early diagnosis and intervention. Artificial intelligence has played a significant role in terms of disease diagnosis and treatments. However, it has got limited acceptance in the medical community due to its lack of transparency. This study aims to advance the understanding and prediction of Alzheimer’s disease progression through the integration of time-aware modeling and Explainable AI techniques. It makes significant contributions by addressing two key objectives in the context of Alzheimer’s disease. First, by including temporal aspects, it accurately depicts the pace at which relevant predictors change over time, thereby capturing the dynamic nature of Alzheimer’s disease. Second, by giving interpretable insights into the algorithm’s decision-making process, the study hopes to empower researchers and physicians. This approach not only enhances transparency but also builds trust in the model’s outcomes. The ADNI dataset, comprising 2980 observations, was employed for developing a prediction model using various machine learning classifiers. Among these classifiers, the Random Forest model emerged as the top performer, exhibiting superior accuracy, a high Coefficient of Determination (R2), and an impressive F1 score. To enhance interpretability, subsequent analyses utilized LIME and SHAP techniques. By combining time-aware modeling with Explainable AI methods, we seek to unravel the dynamic relationships within the dataset, providing transparent insights into the temporal evolution of Alzheimer’s disease. Thus, this paper contributes to the creation of a clinically relevant and practical model for monitoring Alzheimer’s disease progression that holds the potential for a deeper understanding of the evolving nature of the disease and paving the way for personalized and timely interventions. |
| first_indexed | 2025-11-15T01:05:13Z |
| format | Article |
| id | oai:generic.eprints.org:25583 |
| institution | Universiti Kebangasaan Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T01:05:13Z |
| publishDate | 2024 |
| publisher | Penerbit Universiti Kebangsaan Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | oai:generic.eprints.org:255832025-07-14T08:48:31Z http://journalarticle.ukm.my/25583/ Transparent insights into alzheimer’s progression: a time-aware approach with explainable Abraham Varghese, Ben George, Vinu Sherimon, Prashanth Gouda, Alzheimer’s disease, acknowledged for its intricate and degenerative characteristics, presents considerable challenges, particularly among the elderly population. The relentless nature of Alzheimer’s, marked by the gradual deterioration of cognitive function, underscores the urgency to develop effective strategies for early diagnosis and intervention. Artificial intelligence has played a significant role in terms of disease diagnosis and treatments. However, it has got limited acceptance in the medical community due to its lack of transparency. This study aims to advance the understanding and prediction of Alzheimer’s disease progression through the integration of time-aware modeling and Explainable AI techniques. It makes significant contributions by addressing two key objectives in the context of Alzheimer’s disease. First, by including temporal aspects, it accurately depicts the pace at which relevant predictors change over time, thereby capturing the dynamic nature of Alzheimer’s disease. Second, by giving interpretable insights into the algorithm’s decision-making process, the study hopes to empower researchers and physicians. This approach not only enhances transparency but also builds trust in the model’s outcomes. The ADNI dataset, comprising 2980 observations, was employed for developing a prediction model using various machine learning classifiers. Among these classifiers, the Random Forest model emerged as the top performer, exhibiting superior accuracy, a high Coefficient of Determination (R2), and an impressive F1 score. To enhance interpretability, subsequent analyses utilized LIME and SHAP techniques. By combining time-aware modeling with Explainable AI methods, we seek to unravel the dynamic relationships within the dataset, providing transparent insights into the temporal evolution of Alzheimer’s disease. Thus, this paper contributes to the creation of a clinically relevant and practical model for monitoring Alzheimer’s disease progression that holds the potential for a deeper understanding of the evolving nature of the disease and paving the way for personalized and timely interventions. Penerbit Universiti Kebangsaan Malaysia 2024-07 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/25583/1/kejut_34.pdf Abraham Varghese, and Ben George, and Vinu Sherimon, and Prashanth Gouda, (2024) Transparent insights into alzheimer’s progression: a time-aware approach with explainable. Jurnal Kejuruteraan, 36 (4). pp. 1713-1725. ISSN 0128-0198 https://www.ukm.my/jkukm/volume-3604-2024/ |
| spellingShingle | Abraham Varghese, Ben George, Vinu Sherimon, Prashanth Gouda, Transparent insights into alzheimer’s progression: a time-aware approach with explainable |
| title | Transparent insights into alzheimer’s progression: a time-aware approach with explainable |
| title_full | Transparent insights into alzheimer’s progression: a time-aware approach with explainable |
| title_fullStr | Transparent insights into alzheimer’s progression: a time-aware approach with explainable |
| title_full_unstemmed | Transparent insights into alzheimer’s progression: a time-aware approach with explainable |
| title_short | Transparent insights into alzheimer’s progression: a time-aware approach with explainable |
| title_sort | transparent insights into alzheimer’s progression: a time-aware approach with explainable |
| url | http://journalarticle.ukm.my/25583/ http://journalarticle.ukm.my/25583/ http://journalarticle.ukm.my/25583/1/kejut_34.pdf |