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Quantum Computing in Medical Diagnostics: A QSVM Approach to Alzheimer's Disease Classification

JSHS · 2024

Overview

The of classification the requiring challenging, is (AD) Disease Alzheimer's diagnosing accurately of task Mild Dementia, Mild-Very Dementia,-Non stages: distinct into disease the Dementia, and Moderate Dementia. Traditional machine learning (ML) classifiers such as support are networks neuralartificialandforests,randomtrees,decisionneighbors,nearest-kmachines,vector the as However, datasets. Disease Alzheimer’s handling in effective broadly complexity computationally increasingly become classifiers these grow, datasets these of size and in advancements Parallel .algorithms powerful and efficient more for need critical a highlighting intensive, methods.ML traditionaloptimize could technologies quantum that suggest ML and computing quantum computers, classical of constraints computational the overcome topotential thehas computation Quantum a represents learning machine quantum of integration The data. of processing efficient more for allowing enhance substantiallycould datasets intricate andlarge processto capacity its as advancement significant Support Quantuma presentIHerein, .progression diseaseand systems biological of understandingOur a fromimages brain ofMRIs given classificationAD for designed specifically (QSVM), Machine Vector large handleto strengths computing'squantum leverage toaims modelThis .datasetAD preprocessed evaluation thorough aconducted Ieffectively. moreresearch biologicalin patterns complex anddatasets and complexity time analyzing involvedThis .algorithmstraditional several to it comparing QSVM, theof into insights provide findings My score.-F1 and recall, precision, accuracy, as such metricsperformance nuanced a offering performance, comparative its and effectiveness operational model's QSVM the applicability in medical diagnostics and the broader field of ML. its of understanding DoDEA Europe

Competition history

  • JSHS 2024 Category not listed

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