Authors :
Madhuri Badole; Siddhesh Rane; Atharv Bharne; Mayur Karpe
Volume/Issue :
Volume 9 - 2024, Issue 1 - January
Google Scholar :
http://tinyurl.com/32cj8s95
Scribd :
http://tinyurl.com/4h9mh3tj
DOI :
https://doi.org/10.5281/zenodo.10567352
Abstract :
One of the most prevalent diseases in the
world is Alzheimer’s (AD). It is a neurological
condition that can lead to cognitive decline and
memory loss. Both the senior population and the
prevalence of diseases affecting them have
dramatically increased in recent years. It is critical to
categorize the progression of Alzheimer’s disease.
Alzheimer's disease (AD) is a complicated neurological
ailment that progresses in different ways for each
individual. In this study, we present a novel approach
to personalised Alzheimer's disease progression
prediction using machine learning techniques. Our
goal is to create a model that can forecast the stage of
the condition for specific individuals and classify them
into one of four categories: Normal, Mild, Average, or
Critical. Our method uses Convolutional Neural
Networks (CNN) to extract characteristics from
various MRI scans, capturing complex patterns in
Alzheimer's progression. The CNN is extensively
trained on a diverse dataset. Traditional classifiers
such as Support Vector Machines (SVM) and Decision
Trees supplement the CNN, improving the
classification process. Furthermore, ensemble
learning, specifically majority voting, harmonises
predictions from CNN, SVM, and Decision Trees,
increasing accuracy by using their individual strengths
to predict Alzheimer's disease development.
Keywords :
Convolutional Neural Networks (CNNs), Decision Trees, Image Preprocessing, Machine Learning, Support Vector Machine (SVM), Ensemble Learning.
One of the most prevalent diseases in the
world is Alzheimer’s (AD). It is a neurological
condition that can lead to cognitive decline and
memory loss. Both the senior population and the
prevalence of diseases affecting them have
dramatically increased in recent years. It is critical to
categorize the progression of Alzheimer’s disease.
Alzheimer's disease (AD) is a complicated neurological
ailment that progresses in different ways for each
individual. In this study, we present a novel approach
to personalised Alzheimer's disease progression
prediction using machine learning techniques. Our
goal is to create a model that can forecast the stage of
the condition for specific individuals and classify them
into one of four categories: Normal, Mild, Average, or
Critical. Our method uses Convolutional Neural
Networks (CNN) to extract characteristics from
various MRI scans, capturing complex patterns in
Alzheimer's progression. The CNN is extensively
trained on a diverse dataset. Traditional classifiers
such as Support Vector Machines (SVM) and Decision
Trees supplement the CNN, improving the
classification process. Furthermore, ensemble
learning, specifically majority voting, harmonises
predictions from CNN, SVM, and Decision Trees,
increasing accuracy by using their individual strengths
to predict Alzheimer's disease development.
Keywords :
Convolutional Neural Networks (CNNs), Decision Trees, Image Preprocessing, Machine Learning, Support Vector Machine (SVM), Ensemble Learning.
