International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

Frequency: 12

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 Volume 12, Issue 10 (October 2025), Pages: 118-128

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 Original Research Paper

A smart medical assistant robot for explainable AI-based Alzheimer’s disease prediction using big data analytics

 Author(s): 

 Boumedyen Shannaq *

 Affiliation(s):

 Management Information System Department, University of Buraimi, Al Buraimi, Oman

 Full text

    Full Text - PDF

 * Corresponding Author. 

   Corresponding author's ORCID profile:  https://orcid.org/0000-0001-5867-3986

 Digital Object Identifier (DOI)

  https://doi.org/10.21833/ijaas.2025.10.014

 Abstract

This study presents the development of TAER_Robot, an explainable AI (XAI)-based medical assistant for predicting Alzheimer’s Disease (AlzD). The main aim is to integrate Machine Learning (ML) models with explanation techniques to build an accurate and interpretable risk assessment system. The research explores how age, cognitive function, and lifestyle factors influence prediction results, using a dataset of 2,149 records with 33 features such as age, gender, BMI, smoking, and alcohol use. Data preprocessing involved normalization, categorical encoding, and handling missing values. The dataset was split into training and testing sets at ratios of 80/20, 70/30, and 60/40 to identify the best configuration. Random Forest, CatBoost, and XGBoost were used as core ML models, while SHAP and LIME provided interpretability. LightGBM achieved the highest performance, with 95.6% accuracy and a 0.955 ROC-AUC score, exceeding previous models. Further testing confirmed system reliability with up to 94.1% accuracy. TAER_Robot enhances early-stage AlzD prediction by offering both strong performance and transparent decision-making, contributing to the improvement of AI-supported clinical decision systems.

 © 2025 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords

 Alzheimer’s prediction, Explainable AI, Machine learning, Risk assessment, Clinical decision

 Article history

 Received 27 January 2025, Received in revised form 16 May 2025, Accepted 29 September 2025

 Funding

This study was supported by funding provided by the University of Buraimi. 

 Acknowledgment

I sincerely thank the University of Buraimi for its ongoing backing and plentiful financial support during project development. The university's constant dedication to research advancement and innovation played a vital role in finishing this work successfully. This research received crucial support from the University of Buraimi by offering significant resources, direction, and constant encouragement. The valuable backing from the Buraimi University made this project possible, and I express profound gratitude for their dedication to scholarly achievement and research progress. 

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Shannaq B (2025). A smart medical assistant robot for explainable AI-based Alzheimer’s disease prediction using big data analytics. International Journal of Advanced and Applied Sciences, 12(10): 118-128

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 Figures

  Fig. 1  Fig. 2  Fig. 3  Fig. 4  Fig. 5 

 Tables

  Table 1  Table 2  Table 3  Table 4

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