Alzheimer’s Disease Detection Using CNN Based on Effective Dimensionality Reduction Approach

Abstract

In developed countries, Alzheimer’s disease (AD) is one of the major causes of death. Until now, clinically there is not have any diagnostic method available but from a research point of view, this disease detection accuracy is produced by computational algorithms. There are many researchers who are working to find about Alzheimer’s disease property, its stages, and classification ways. This research plays a vital role in clinical tests for medical researchers and in the overall medical sector. One of the major problems found by the researchers in the field is the large data dimension. In the study, we proposed an efficient dimensionality reduction method to improve Alzheimer’s dis-ease (AD) detection accuracy. To implement the method first we cleaned the dataset to remove the null value and removing other unacceptable data by some preprocessing tasks. On the preprocessed data first, we have split into training and test dataset then we employed a dimension reduction method and there-fore applied a machine learning algorithm on the reduced dataset to produce accuracy for detecting Alzheimer’s disease. To overserve and calculate the accuracy we computed confusion matrix, precision, recall, f1-score value and finally accuracy of the method as well. Reducing the dimension of data here we applied consequently Principle component analysis (PCA), Random Projection (RP) and Feature Agglomeration (FA). On the reduced features, we have applied the Random Forest (RF) and Convolution neural network (CNN) machine-learning algorithm based on the dimensionality reduction method. To evaluate our proposed methodology here we have used Alzheimer’s disease neuroimaging initiative (ADNI) dataset. We have experimented for (i) Random forest with principal component analysis (RFPCA), (ii) Convolution neural network (CNN) with (PCA) (CNNPCA), (iii) Random forest with Ran-dom projection(RFRR), and (iv) Random forest with Feature agglomeration(RFFA) to differentiate patients with the AD from healthy patients. Our model namely Random forest with Random projection(RFRP) has produced 93% accuracy. We believe that our work will be recognized as a groundbreaking discovery in this domain.