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Early-Stage Alzheimer’s Disease Prediction Using Machine Learning Models
Open accessAbstract
Alzheimer's disease (AD) is the leading cause of dementia in older adults. There is currently a lot of interest in applying machine learning to find out metabolic diseases like Alzheimer's and Diabetes that affect a large population of people around the world. Their incidence rates are increasing at an alarming rate every year. In Alzheimer's disease, the brain is affected by neurodegenerative changes. As our aging population increases, more and more individuals, their families, and healthcare will experience diseases that affect memory and functioning. These effects will be profound on the social, financial, and economic fronts. In its early stages, Alzheimer's disease is hard to predict. A treatment given at an early stage of AD is more effective, and it causes fewer minor damage than a treatment done at a later stage. Several techniques such as Decision Tree, Random Forest, Support Vector Machine, Gradient Boosting, and Voting classifiers have been employed to identify the best parameters for Alzheimer's disease prediction. Predictions of Alzheimer's disease are based on Open Access Series of Imaging Studies (OASIS) data, and performance is measured with parameters like Precision, Recall, Accuracy, and F1-score for ML models. The proposed classification scheme can be used by clinicians to make diagnoses of these diseases. It is highly beneficial to lower annual mortality rates of Alzheimer's disease in early diagnosis with these ML algorithms. The proposed work shows better results with the best validation average accuracy of 83% on the test data of AD. This test accuracy score is significantly higher in comparison with existing works.
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“Achieved 83% accuracy on test data”
From Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions (2023) · cites this paper as [65]
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“likely to have dementia than women. Men are more”
From Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions (2023) · cites this paper as [65]
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Self-citation
Metadata
- Authors
- Kavitha, C., Mani, V., Srividhya, S. R., Khalaf, O. I., & Tavera Romero, C. A.
- Affiliations
- Sathyabama Institute of Science and Technology (IN)
- Nahrain University (IQ)
- Corresponding author
- Kavitha, C.
- Venue
- Frontiers in Public Health
- Publisher
- Frontiers Media SA
- Published
- 2022-03-03
- Volume
- 10
- Pages
- 853294-853294
- ISSN
- 2296-2565 (ISSN-L)
- PMC
- PMC8927715
- OpenAlex
- W4214898695
- Cited by
- 373
- References
- 30
- Citations by year
- 2026: 64 · 2025: 135 · 2024: 90 · 2023: 56 · 2022: 28
- FWCI
- 50.29
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Public Health (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Universidad Santiago de Cali (01-2021)
- National Institute on Aging (P01AG003991)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Alzheimer's disease research and treatments
- Keywords
- Alzheimer's disease, dementia, OASIS dataset, neuroimaging, machine learning classification, random forest, gradient boosting, support vector machine, decision tree, voting classifier, early diagnosis
- MeSH terms
- Machine Learning, Aged, Alzheimer Disease, Brain, Humans, Magnetic Resonance Imaging, Cognitive Dysfunction
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