Computing Sciences and Computer Engineering
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).
IEEE Journal of Translational Engineering in Health and Medicine
(2019). Analyze Informant-Based Questionnaire for the Early Diagnosis of Senile Dementia Using Deep Learning. IEEE Journal of Translational Engineering in Health and Medicine.
Available at: https://aquila.usm.edu/fac_pubs/16854