An Optimized Prediction Of Alzheimer’s Disease Using Deep Convoltional Neural Network

Abstract

Deep Learning is a subspace of machine learning that analyzes the data with the logic of human using the algorithmic structure called Artificial Neural Network (ANN). They are highly employed in healthcare which identifies the pattern of patient’s symptoms. Alzheimer’s Disease(AD) is one of the neurogenerative disorders that is being affecting many people globally. The disease is segregated into several stages and is classified mainly as Mild, Moderate, or Severe. The symptoms includes crippled speaking and writing, inability in remembering the information and so on. Machine learning algorithm techniques such as Independent Component Analysis (ICA), Decision Tree Classifier, and Linear Discriminant Analysis (LDA), have been used to pinpoint different stages of disease, but the accuracy of identification is not considered to be good. This paper proposes a Deep Learning-based technique where the prediction is improved by employing a Convolutional Neural Network (CNN). It examines the EEG signal, features are extracted using the Fast Fourier Transform (FFT), and classification of the disease using CNN. To optimize and increase the accuracy, Adam optimization algorithm is implemented. Adam can easily handle the sparse gradients on noisy problems. It uses Momentum and Adaptive Learning rate for fast convergence..