A deep learning model for Alzheimer’s disease diagnosis based on patient clinical records
Open accessAbstract
BACKGROUND: Dementia, with Alzheimer's disease (AD) being the most common type of this neurodegenerative disease, is an under-diagnosed health problem in older people. The creation of classification models based on AD risk factors using Deep Learning is a promising tool to minimize the impact of under-diagnosis. OBJECTIVE: To develop a Deep Learning model that uses clinical data from patients with dementia to classify whether they have AD. METHODS: A Deep Learning model to identify AD in clinical records is proposed. In addition, several rebalancing methods have been used to preprocess the dataset and several studies have been carried out to tune up the model. RESULTS: Model has been tested against other well-established machine learning techniques, having better results than these in terms of AUC with alpha less than 0.05. CONCLUSIONS: The developed Neural Network Model has a good performance and can be an accurate assisting tool for AD diagnosis.
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Ávila-Jiménez et al. (2024) pioneered a deep learning model using ANNs to identify subtle disease patterns in comprehensive medical records, advancing early AD detection.
“Ávila-Jiménez et al. (2024). pioneered a deep learning model using ANNs to identify subtle disease patterns in comprehensive medical records, thus significantly advancing early AD detection”
From Multimodal neuroimaging and AI integration in cognitive disorders: advances, challenges, and future directions for precision medicine (2026) · cites this paper as Ávila-Jiménez et al., 2024
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- Authors
- Ávila-Jiménez, J. L., Cantón-Habas, V., Carrera-González, M. D. P., Rich-Ruiz, M., & Ventura, S.
- Affiliations
- University of Córdoba (ES)
- Hospital Universitario Reina Sofía (ES)
- Corresponding author
- Cantón-Habas, Vanesa
- Venue
- Computers in Biology and Medicine
- Publisher
- Elsevier BV
- Published
- 2024-02
- Volume
- 169
- Pages
- 107814
- ISSN
- 0010-4825 (ISSN-L)
- OpenAlex
- W4389505698
- Cited by
- 50
- References
- 42
- Citations by year
- 2026: 7 · 2025: 25 · 2024: 18
- FWCI
- 10.16
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Computers in Biology and Medicine (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- RUJA. Institutional Repository of Scientific Production of the University of Jaén (University of Jaén) (publishedVersion, cc-by-nc-nd)
- Universidad de Córdoba Insitutional Repository (Universidad de Córdoba) (submittedVersion, cc-by-nc-nd)
- Funders
- Ministerio de Ciencia e Innovación (PID2020-115832GB-I00, PID2020)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- Alzheimer's disease, dementia diagnosis, medical records, deep learning, artificial neural networks, class imbalance, data rebalancing, risk factors, underdiagnosis, older adults
- MeSH terms
- Deep Learning, Aged, Alzheimer Disease, Humans, Magnetic Resonance Imaging, Neurodegenerative Diseases, Neuroimaging
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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