Fubao Zhu1, Xiaonan Li1, Daniel Mcgonigle2, Haipeng Tang2, Zhuo He3, Chaoyang Zhang2, Guang-Uei Hung4, Pai-Yi Chiu5, Weihua Zhou3. 1. 1School of Computer and Communication EngineeringZhengzhou University of Light IndustryZhengzhou450002China. 2. 2School of Computing Sciences and Computer EngineeringUniversity of Southern MississippiLong BeachMS39560USA. 3. 3College of ComputingMichigan Technological UniversityHoughtonMI49931USA. 4. 4Department of Nuclear MedicineChang Bing Show Chwan Memorial HospitalChanghua505Taiwan. 5. 5Department of NeurologyShow Chwan Memorial HospitalChanghua500Taiwan.
Abstract
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).
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).
Entities:
Keywords:
Dementia; deep neural network; information gain; machine learning