Renaissance for Alzheimer’s Disease Detection using ML DL Techniques

Abstract

The early diagnosis of Alzheimer’s disease (AD) is very important in ensuring proper intervention in a timely manner so that patients can be managed better and have better outcomes. Earlier, cognitivedata and numerical patient information were used with conventional machine learning (ML) methods for early detection. One of the past ensemble-based approaches trained up to seven ML classifiers that included Decision Tree, Random Forest, SVM, ANN, and AdaBoost, gaining 93.92% accuracy. However, deep learning integration and optimal feature extraction was not present in that study. This study proposes a novel deep learning (DL) approach using advanced feature selection by Decision Tree and Random Forest, and Synthetic Minority Over-Sampling Technique (SMOTE) for class balancing. A hybrid deep learning approach based on Convolutional Neural Networks (CNN) and Artificial Neural Networks (ANN) is presented. The model provides efficient, scalable, and adaptive, and a robust Alzheimer’s disease (AD) detection for the real-life patients. The models are trained successfully with patients' data from Kaggle.The model provides efficient early AD detection with better accuracy and stability.