Abstract:
Alzheimer’s disease (AD) is a neurodevelopmental impairment that results in a person’s behavior, thinking, and
memory loss. The most common symptoms of AD are losing memory and early aging. In addition to these, there are
several serious impacts of AD. However, the impact of AD can be mitigated by early-stage detection though it cannot
be cured permanently. Early-stage detection is the most challenging task for controlling and mitigating the impact
of AD. The study proposes a predictive model to detect AD in the initial phase based on machine learning and a
deep learning approach to address the issue. To build a predictive model, open-source data was collected where five
stages of images of AD were available as Cognitive Normal (CN), Early Mild Cognitive Impairment (EMCI), Mild
Cognitive Impairment (MCI), Late Mild Cognitive Impairment (LMCI), and AD. Every stage of AD is considered
as a class, and then the dataset was divided into three parts binary class, three class, and five class. In this research, we
applied different preprocessing steps with augmentation techniques to efficiently identify AD. It integrates a random
oversampling technique to handle the imbalance problem from target classes, mitigating the model overfitting
and biases. Then three machine learning classifiers, such as random forest (RF), K-Nearest neighbor (KNN), and
support vector machine (SVM), and two deep learning methods, such as convolutional neuronal network (CNN)
and artificial neural network (ANN) were applied on these datasets. After analyzing the performance of the used
models and the datasets, it is found that CNN with binary class outperformed 88.20% accuracy. The result of the
study indicates that the model is highly potential to detect AD in the initial phase.