DEVELOPMENT OF IMPROVED WEIGHED QUANTUM LION OPTIMIZATION FOR
ALZHEIMER'S DISEASE WITH SMOOTH SUPPORT VECTOR MACHINE
Abstract
Accurate diagnosis of Alzheimer’s disease(AD) and Mild Cognitive
Impairment(MCI) was identified on an early stage is essential in the
healthcare industry to stop degeneration. The Smooth Support Vector
Machine (SSVM) model, Principal Component Analysis (PCA), feature
extraction, and Magnetic Resonance Imaging (MRI) image preprocessing are
the components for the diagnosis of AD is proposed in this research at
early stage. To assist in the classifier’s training, we proposed a novel
Improved Weighed Quantum Lion Optimization (IWQLO). The SSVM parameters
are specifically proposed to be optimized using a new Switching delayed
Lion Optimization (SLO) algorithm.The IWQLO-SSVM approach was
effectively used to classify AD and MCI utilizing MRI scans of the
[Alzheimer’s disease Neuroimaging
Initiative](https://adni.loni.usc.edu/)(ADNI) database and Outcome and
Assessment Information Set (OASIS) database. For six example scenarios,
the classification accuracy of our proposed method is acceptable.
Testing show that the proposed approach improves the performance
measures such as accuracy, precision, specificity, sensitivity and
recallfor detecting the early stage AD diagnosis.