Performance Evaluation of Quantum Machine Learning and Classical Machine Learning Techniques for Alzheimer's Disease Diagnosis
Keywords:
Alzheimer’s Disease, Quantum ML, Machine Learning, SVM, QSVM, Disease Diagnosis, Hippocampus.Abstract
Alzheimer's Disease (AD) first affects the brain parts that are associated with learning. It is one of the most prevalent forms of Dementia. Its early diagnosis is crucial for properly managing the disease treatment because it is chronic and irreversible. This paper introduces a holistic approach to achieve early detection of AD using the hippocampus and transfer learning. Here we have compared the results from the classical machine learning model – Support Vector Machine (SVM) and the Quantum Support Vector Machine (QSVM) model. QSVM - a quantum variant of standard SVM algorithm. It is developed using techniques such as quantum kernel estimation in quantum computing for more efficient processing of high-dimensional data than standard SVMs.. The most common symptoms of Alzheimer’s are loss of memory and cognitive impairment. This stems from the destruction and death of nerve cells in the brain related to memory. Mild Cognitive Impairment (MCI) is a condition between normal brain function and Alzheimer’s. From the prodromal MCI stage, it progresses gradually to dementia. Several studies show that Alzheimer’s develops at a rate of 10–15% per year from MCI. The early identification of patients with MCI may halt/delay the progression from MCI stage to Alzheimer’s. The results with the precision of 0.85 with the QSVM compared to the precision of 0.78 and recall of 1.00 compared to the 0.89 with the traditional SVM shows that our technique of Quantum Machine Learning (QML) is very useful in Alzheimer’s early diagnosis.
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