Early-Stage Alzheimer's Disease Prediction Using Machine Learning Models

Topic: Amyloid, tau and blood-test diagnosis

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.

Key findings

• 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.

Why it matters

This study used several machine learning classifiers on imaging data to predict early-stage Alzheimer's disease, with the best validation average accuracy of 83%. It is a prediction study and does not test any treatment, cure or prevention.

New to this subject? Start with our plain-English guide: Digital health →

Study details

• Evidence strengthLow

Read the original

• DOI: 10.3389/fpubh.2022.853294
• PubMed: 35309200
• Original source →
• Free full text: Frontiers in Public Health →

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

@article{kavitha2022-35309200,
  author  = {Kavitha, C. and Mani, V. and Srividhya, SR. and Khalaf, O. I. and Tavera Romero, C. A.},
  title   = {{Early-Stage Alzheimer's Disease Prediction Using Machine Learning Models}},
  journal = {Frontiers in public health},
  year    = {2022},
  doi     = {10.3389/fpubh.2022.853294},
  pmid    = {35309200},
  url     = {https://doi.org/10.3389/fpubh.2022.853294}
}

Studies this one cites

Referenced by this study and also in the alzhub library, so you can read them here.

• Lifestyle for Brain Health (LIBRA): a new model for dementia prevention International journal of geriatric psychiatry · 2017-02-28 · usefulness score 69/100 · Prevention and risk
• Dementia prevention, intervention, and care The Lancet · 2017-07-20 · usefulness score 65/100 · Prevention and risk
• Target risk factors for dementia prevention: a systematic review and Delphi consensus study on the evidence from observational studies International journal of geriatric psychiatry · 2014-12-12 · usefulness score 62/100 · Prevention and risk
• Modifiable Risk Factors for Prevention of Dementia in Midlife, Late Life and the Oldest-Old: Validation of the LIBRA Index Journal of Alzheimer's disease : JAD · 2017-01-01 · usefulness score 56/100 · Prevention and risk

Later studies that cite this one

7 studies in the alzhub library list this one in their references.

• Using machine learning and electronic health record (EHR) data for the early prediction of Alzheimer's Disease and Related Dementias The Journal of Prevention of Alzheimer s Disease · 2025-04-16 · usefulness score 56/100 · How common it is
• Explainable AI-based Alzheimer's prediction and management using multimodal data PloS one · 2023-11-16 · usefulness score 53/100 · Digital health tools
• Explainable machine learning for predicting longitudinal dementia status: Establishing a leakage-free benchmark PLOS Digital Health · 2026-05-18 · usefulness score 50/100 · Digital health tools
• Advances in Alzheimer’s disease diagnosis with machine learning and deep learning techniques: a comprehensive review Artificial Intelligence Review · 2026-02-04 · usefulness score 41/100 · Digital health tools
• Investigating dementia patterns by machine learning and graph-based marker analysis for multiclass dementia diagnosis Discover Computing · 2026-05-18 · usefulness score 35/100 · Getting a diagnosis
• Alzheimer disease predicting from clinical and MRI data using DeepALZNET dual pathway framework Scientific Reports · 2025-12-04 · usefulness score 35/100 · Brain scans and imaging
• Alzheimers and Brain Tumor Detection Using Deep Learning International Journal of Innovative Science and Research Technology · 2025-05-02 · usefulness score 28/100 · Brain scans and imaging

Related studies in Digital health tools

• A machine learning framework for early-stage dementia-friendly architectural design evaluation using visual access metrics Architectural Intelligence · 2025-07-10 · usefulness score 29/100
• Multimodal deep learning models for early detection of Alzheimer's disease stage Scientific reports · 2021-02-05 · usefulness score 43/100
• Exploring Factors Related to Social Isolation Among Older Adults in the Predementia Stage Using Ecological Momentary Assessments and Actigraphy: Machine Learning Approach Journal of Medical Internet Research · 2025-06-23 · usefulness score 42/100
• Benchmarking machine learning models for late-onset alzheimer's disease prediction from genomic data BMC bioinformatics · 2019-12-16 · usefulness score 43/100
• Early Prediction of Parkinson’s Disease Progression by Integrating Research Cohort and Real-World Data Using Knowledge-Anchored Graph Learning medRxiv · 2026-07-09 · usefulness score 16/100
• Early dementia diagnosis, MCI-to-dementia risk prediction, and the role of machine learning methods for feature extraction from integrated biomarkers, in particular for EEG signal analysis Alzheimer's & dementia : the journal of the Alzheimer's Association · 2022-04-07 · usefulness score 49/100

Similar studies

• Prediction Of Alzheimer Patients with Machine Learning Algorithms 2025 · Genetics
• Machine learning prediction of incidence of Alzheimer's disease using large-scale administrative health data 2020 · Digital health tools
• Early diagnosis of Alzheimer's disease using machine learning: a multi-diagnostic, generalizable approach 2022 · Digital health tools
• Machine learning for comprehensive forecasting of Alzheimer's Disease progression 2019 · Mild cognitive impairment
• Exploiting machine learning models to identify novel Alzheimer's disease biomarkers and potential targets 2023 · Digital health tools
• Advancing Alzheimer’s disease risk prediction: development and validation of a machine learning-based preclinical screening model in a cross-sectional study 2025 · Digital health tools

More on Digital health tools

• A review that covers this ground Clinical prediction models using artificial intelligence approaches in dementia Aging Clinical and Experimental Research · 2025-07-25 · usefulness score 59/100
• Newest in this subject Speaker-Wise Dementia Screening fromNaturalistic Speech with Lexical Entropy, Semantic Drift, and Multimodal Fusion Research Square · 2026-10-21 · Preprint — not yet peer-reviewed · usefulness score 16/100
• Newest in this subject Integrating Multisource Target Evidence with Machine-Learning Potency Prediction for Alzheimer’s Disease Drug Discovery bioRxiv · 2026-10-20 · Preprint — not yet peer-reviewed · usefulness score 16/100

See all studies in Digital health tools →