Improving Dementia Prediction Using Ensemble Majority Voting Classifier

Early detection of dementia patients in advance is a great concern for the physicians. That is why physicians make use of multi modal data to accomplish this. The baseline visit data of the patients are mainly utilized for this task. Modern Machine Learning techniques provide empirical evidence based approach to physicians for predicting the diagnosis status of the patients. This paper proposes an ensemble majority voting classifier approach for improving the detection of dementia using baseline visit data. The ensemble model consists of Logistic Regression, Random Forest, and Naive Bayes Classifiers. The proposed ensemble classifier reported with a BCA, F1-score of 92%, 0.92 for classifying demented and non-demented patients. Our results suggest that the prediction using the ensemble majority voting classifier improves the Balanced Classification Accuracy, F1-score for predicting dementia on the multi modal data of Open Access Series Imaging Dataset. The results using ensemble models are promising and highlight the importance of using ensemble models for dementia detection using multimodal data.