Article in Press
This article is currently in the Journal Preview phase. The final published version may have formatting changes or additional corrections.
Abstract
Alzheimer's disease is a progressive neurodegenerative disorder that impacts millions of people worldwide and the early diagnosis of this is crucial. The project focuses on Alzheimer's diagnosis and wellness optimization through the implementation of machine learning (ML) techniques. The primary objective is to develop a mobile application that can aid in early detection of Alzheimer's disease along with better accuracy. This will help the doctors for diagnosis and recommending wellness strategies. Doctors can schedule the appointments and the patient will get the notifications. Person can keep a daily routine using a schedule planner and get notified for the same. The progress of the person is tracked and the results are shown statistically for better understanding. MRI image dataset along with a set of cognitive tests will help to
increase the accuracy. The mobile app provides advice on nutrition recommendations. The ML algorithms employed in this include Convolutional Neural Network (CNN), Recurrent Neural Network (RNN). Mobile technologies such as android studio, flutter and React Native will be used. The firebase will be used to provide security for the server data storage and authentication. Dataset of MRI images from kaggle will be used to train and test the model.
