ArticleIEEE journal of translational engineering in health and medicine2020
Analyze Informant-Based Questionnaire for The Early Diagnosis of Senile Dementia Using Deep Learning.
Article in IEEE journal of translational engineering in health and medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.
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Who cites it
16 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine learning based algorithms for virtual early detection and screening of neurodegenerative and neurocognitive disorders: a systematic-review.Frontiers in neurology · 2024Pooled it
- Neural Computation-Based Methods for the Early Diagnosis and Prognosis of Alzheimer's Disease Not Using Neuroimaging Biomarkers: A Systematic Review.Journal of Alzheimer's disease : JAD · 2024Pooled it
- Exploring Voice Acoustic Features Associated with Cognitive Status in Korean Speakers: A Preliminary Machine Learning Study.Diagnostics (Basel, Switzerland) · 2024Article
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- Synthetic data analysis for early detection of Alzheimer progression through machine learning algorithms.PeerJ. Computer science · 2024Article
- Hypothesis testing procedure for binary and multi-class FStatistics in medicine · 2023Article
- Minimal amount of exercise prevents incident dementia in cognitively normal older adults with osteoarthritis: a retrospective longitudinal follow-up study.Scientific reports · 2023Article
- Prediction of ADMET Properties of Anti-Breast Cancer Compounds Using Three Machine Learning Algorithms.Molecules (Basel, Switzerland) · 2023Article
- Real-World Testing of a Machine Learning-Derived Visual Scale for Tc99m TRODAT-1 for Diagnosing Lewy Body Disease: Comparison with a Traditional Approach Using Semiquantification.Journal of personalized medicine · 2022Article
- Confidence interval for micro-averagedApplied intelligence (Dordrecht, Netherlands) · 2022Article
- Sum of boxes of the clinical dementia rating scale highly predicts conversion or reversion in predementia stages.Frontiers in aging neuroscience · 2022Article
- Emergency department visits among people with predementia highly predicts conversion to dementia.PloS one · 2022Article
- Synuclein Motor Dysfunction Composite Scale for the Discrimination of Dementia With Lewy Bodies From Alzheimer's Disease.Frontiers in aging neuroscience · 2022Article
- An Ontology-Based Chatbot to Enhance Experiential Learning in a Cultural Heritage Scenario.Frontiers in artificial intelligence · 2022Article
- Operational Determination of Subjective Cognitive Decline, Mild Cognitive Impairment, and Dementia Using Sum of Boxes of the Clinical Dementia Rating Scale.Frontiers in aging neuroscience · 2021Article
- Motor Dysfunction Questionnaire and Dopamine Transporter Imaging Composite Scale Improve Differentiating Dementia With Lewy Bodies From Alzheimer's Disease With Motor Dysfunction.Frontiers in aging neuroscience · 2021Article
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Authors and funding
9 authors.
Funding
Abstract
objectiveThis paper proposes a multiclass deep learning method for the classification of dementia using an informant-based questionnaire.
methodsA deep neural network classification model based on Keras framework is proposed in this paper. To evaluate the advantages of our proposed method, we compared the performance of our model with industry-standard machine learning approaches. We enrolled 6,701 individuals, which were randomly divided into training data sets (6030 participants) and test data sets (671 participants). We evaluated each diagnostic model in the test set using accuracy, precision, recall, and F1-Score.
resultsCompared with the seven conventional machine learning algorithms, the DNN showed higher stability and achieved the best accuracy with 0.88, which also showed good results for identifying normal (F1-score = 0.88), mild cognitive impairment (MCI) (F1-score = 0.87), very mild dementia (VMD) (F1-score = 0.77) and Severe dementia (F1-score = 0.94).
conclusionThe deep neural network (DNN) classification model can effectively help doctors accurately screen patients who have normal cognitive function, mild cognitive impairment (MCI), very mild dementia (VMD), mild dementia (Mild), moderate dementia (Moderate), and severe dementia (Severe).
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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.