Abstract:
Alzheimer’s disease (AD) is a type of brain malfunction mainly found in old-aged people; it
is irreversible and develops over time. Usually, people with AD experience initial memory
loss, ultimately leading to their declined cognitive abilities like poor decision making and
problem-solving abilities. However, there is no specific technique available which can predict
the early detection of AD. The neuro-imaging technology that was recently adopted for the
prediction of AD is based on few biomarkers, thus it is time taking. Therefore, it is essential
to bring about an automated technique for AD prediction. The early prediction of AD can be
pivotal in the survival of patients and will prove helpful for the doctors. In this work, we
propose an automated prognosis system based on Machine Learning (ML) techniques for the
prediction of AD. In the proposed model, the Neuro Psychological Measures and Magnetic
Resonance Imaging (MRI) biomarkers (feature vector) are computed and passed to Recurrent
Neural Network (RNN). In RNN, we use Long Short-Term Memory (LSTM). This model
will predict the biomarkers (feature vector) of patients after 6, 12, 18, 24 & 36 months and
will give the predicted values after these time intervals. These predicted biomarkers will go
through fully connected neural network layers. The NN layers will then predict that these
RNN predicted biomarkers belong to an AD patient or a Mild Cognitive Impairment (MCI)
patient. If RNN predicted biomarkers belong to AD, then the patient is converted from MCI
to AD. The developed methodology has been tested on a publicly available data set ADNI
and achieved an accuracy of 88.24% which is better than the other state of the art techniques.
