Objective: This paper proposes a multiclass deep learning method for the classification of dementia using an informant-based questionnaire. Methods: A 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. Results: Compared 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). Conclusion: The 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). INDEX TERMS Dementia, information gain, deep neural network, machine learning.
CME Educational Objectives
1.
Identify classic presentation of vascular dementia.
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Know the most effective treatments for vascular dementia and when they are indicated.
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Understand the impact of medical comorbidities on vascular dementia.
The patient is an 83-year-old white man who is widowed and a former professor with a PhD in education, now retired and living in assisted senior housing due to impairments resulting from Alzheimer’s dementia. His medical history is significant for hypertension, type 2 diabetes mellitus, coronary artery disease, and colon cancer in remission.
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