Leveraging explainable artificial intelligence with ensemble of deep learning model for dementia prediction to enhance clinical decision support systems

Topic: Risk, prevention and care in population studies

The prevalence of dementia is growing worldwide due to the fast ageing of the population. Dementia is an intricate illness that is frequently produced by a mixture of genetic and environmental risk factors. There is no treatment for dementia yet; therefore, the early detection and identification of persons at greater risk of emerging dementia becomes crucial, as this might deliver an opportunity to adopt lifestyle variations to decrease the risk of dementia. Many dementia risk prediction techniques to recognize individuals at high risk have progressed in the past few years. Accepting a structure uniting explainability in artificial intelligence (XAI) with intricate systems will enable us to classify analysts of dementia incidence and then verify their occurrence in the survey as recognized or suspected risk factors. Deep learning (DL) and machine learning (ML) are current techniques for detecting and classifying dementia and making decisions without human participation. This study introduces a Leveraging Explainability Artificial Intelligence and Optimization Algorithm for Accurate Dementia Prediction and Classification Model (LXAIOA-ADPCM) technique in medical diagnosis. The main intention of the LXAIOA-ADPCM technique is to progress a novel algorithm for dementia prediction using advanced techniques. Initially, data normalization is performed by utilizing min-max normalization to convert input data into a beneficial format. Furthermore, the feature selection process is performed by implementing the naked mole-rat algorithm (NMRA) model. For the classification process, the proposed LXAIOA-ADPCM model implements ensemble classifiers such as the bidirectional long short-term memory (BiLSTM), sparse autoencoder (SAE), and temporal convolutional network (TCN) techniques. Finally, the hyperparameter selection of ensemble models is accomplished by utilizing the gazelle optimization algorithm (GOA) technique. Finally, the Grad-CAM is employed as an XAI technique to enhance transparency by providing human-understandable insights into their decision-making processes. A broad array of experiments using the LXAIOA-ADPCM technique is performed under the Dementia Prediction dataset. The simulation validation of the LXAIOA-ADPCM technique portrayed a superior accuracy output of 95.71% over existing models.

Key findings

• For the classification process, the proposed LXAIOA-ADPCM model implements ensemble classifiers such as the bidirectional long short-term memory (BiLSTM), sparse autoencoder (SAE), and temporal convolutional network (TCN) techniques.
• Finally, the hyperparameter selection of ensemble models is accomplished by utilizing the gazelle optimization algorithm (GOA) technique.
• Finally, the Grad-CAM is employed as an XAI technique to enhance transparency by providing human-understandable insights into their decision-making processes.
• A broad array of experiments using the LXAIOA-ADPCM technique is performed under the Dementia Prediction dataset.
• The simulation validation of the LXAIOA-ADPCM technique portrayed a superior accuracy output of 95.71% over existing models.

Why it matters

This paper proposes a deep learning and explainable artificial intelligence model for predicting and classifying dementia to support clinical decisions. It reports no tested treatment, cure, or prevention.

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Study details

• Effect size95.71%
• Evidence strengthLow

Read the original

• DOI: 10.1038/s41598-025-97102-3
• PubMed: 40360623
• Original source →
• Free full text: Scientific Reports →

Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).

@article{medani2025-40360623,
  author  = {Medani, Mohamed and Elhessewi, Ghada Moh. Samir and Alqahtani, Mohammed Abdulrahman and ASKLANY, SOMIA A. and Alamro, Sulaiman and Albalawneh, Da’ad and Alshammeri, Menwa and Assiri, Mohammed Abdullah},
  title   = {{Leveraging explainable artificial intelligence with ensemble of deep learning model for dementia prediction to enhance clinical decision support systems}},
  journal = {Scientific Reports},
  year    = {2025},
  doi     = {10.1038/s41598-025-97102-3},
  pmid    = {40360623},
  url     = {https://doi.org/10.1038/s41598-025-97102-3}
}

Studies this one cites

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• A comparison of machine learning methods for survival analysis of high-dimensional clinical data for dementia prediction Scientific reports · 2020-11-23 · usefulness score 67/100 · Digital health tools
• Performance of Machine Learning Algorithms for Predicting Progression to Dementia in Memory Clinic Patients JAMA network open · 2021-12-01 · usefulness score 66/100 · Digital health tools
• Machine Learning for Dementia Prediction: A Systematic Review and Future Research Directions Journal of medical systems · 2023-02-01 · usefulness score 62/100 · Digital health tools
• Using Machine Learning to Predict Dementia from Neuropsychiatric Symptom and Neuroimaging Data Journal of Alzheimer's disease : JAD · 2020-01-01 · usefulness score 56/100 · Digital health tools
• Predicting Cognitive Impairment and Dementia: A Machine Learning Approach Journal of Alzheimer's disease : JAD · 2020-01-01 · usefulness score 49/100 · Digital health tools

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