ArticleJournal of healthcare engineering2021
A Comparative Analysis of Machine Learning Algorithms to Predict Alzheimer's Disease.
Article in Journal of healthcare engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
24 citing papers in PubMed, 155 citations in OpenAlex.
- Comparative analysis of supervised and ensemble models with unsupervised exploration for alzheimer's disease prediction.Scientific reports · 2026Article
- Use of explainable machine learning in risk classification of Cesarean section delivery: A cross-sectional analysis of Demographic and Health Surveys from ten Sub-Saharan African countries (2016-2024).PLOS global public health · 2026Article
- Multi-Target Drug Design in Alzheimer's Disease Treatment: Emerging Technologies, Advantages, Challenges, and Limitations.Pharmacology research & perspectives · 2025Review
- A Meta-Learning-Based Ensemble Model for Explainable Alzheimer's Disease Diagnosis.Diagnostics (Basel, Switzerland) · 2025Article
- Evaluation of machine learning models for the prediction of Alzheimer's: In search of the best performance.Brain, behavior, & immunity - health · 2025Article
- AlzheimerViT: harnessing lightweight vision transformer architecture for proactive Alzheimer's screening.Frontiers in medicine · 2025Article
- Artificial intelligence technologies for enhancing neurofunctionalities: a comprehensive review with applications in Alzheimer's disease research.Frontiers in aging neuroscience · 2025Review
- A proficient approach for the classification of Alzheimer's disease using a hybridization of machine learning and deep learning.Scientific reports · 2024Article
- Efficient Explainable Models for Alzheimer's Disease Classification with Feature Selection and Data Balancing Approach Using Ensemble Learning.Diagnostics (Basel, Switzerland) · 2024Article
- Predicting the Progression of Mild Cognitive Impairment to Alzheimer's Dementia Using Recurrent Neural Networks With a Series of Neuropsychological Tests.Journal of clinical neurology (Seoul, Korea) · 2024Article
- Article
- Advanced interpretable diagnosis of Alzheimer's disease using SECNN-RF framework with explainable AI.Frontiers in artificial intelligence · 2024Article
- Artificial intelligence technology in Alzheimer's disease research.Intractable & rare diseases research · 2023Review
- Immediate word recall in cognitive assessment can predict dementia using machine learning techniques.Alzheimer's research & therapy · 2023Article
- Leveraging Computational Intelligence Techniques for Diagnosing Degenerative Nerve Diseases: A Comprehensive Review, Open Challenges, and Future Research Directions.Diagnostics (Basel, Switzerland) · 2023Review
- Classification of Alzheimer's disease and frontotemporal dementia using routine clinical and cognitive measures across multicentric underrepresented samples: A cross sectional observational study.Lancet regional health. Americas · 2023Article
- Logistic random forest boosting technique for Alzheimer's diagnosis.International journal of information technology : an official journal of Bharati Vidyapeeth's Institute of Computer Applications and Management · 2023Article
- Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment.Healthcare (Basel, Switzerland) · 2022Article
- Tensor-Based Multi-Modality Feature Selection and Regression for Alzheimer's Disease Diagnosis.Computer science & information technology · 2022Article
- Comparison of Machine Learning Algorithms in the Prediction of Hospitalized Patients with Schizophrenia.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 4 institutions in 3 countries.
Funding
Abstract
Alzheimer's disease has been one of the major concerns recently. Around 45 million people are suffering from this disease. Alzheimer's is a degenerative brain disease with an unspecified cause and pathogenesis which primarily affects older people. The main cause of Alzheimer's disease is Dementia, which progressively damages the brain cells. People lost their thinking ability, reading ability, and many more from this disease. A machine learning system can reduce this problem by predicting the disease. The main aim is to recognize Dementia among various patients. This paper represents the result and analysis regarding detecting Dementia from various machine learning models. The Open Access Series of Imaging Studies (OASIS) dataset has been used for the development of the system. The dataset is small, but it has some significant values. The dataset has been analyzed and applied in several machine learning models. Support vector machine, logistic regression, decision tree, and random forest have been used for prediction. First, the system has been run without fine-tuning and then with fine-tuning. Comparing the results, it is found that the support vector machine provides the best results among the models. It has the best accuracy in detecting Dementia among numerous patients. The system is simple and can easily help people by detecting Dementia among them.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.