Unveiling the Power of Collective Intelligence: A Voting-based Approach for Dementia Classification

Abstract

Dementia, a debilitating neurodegenerative condition affecting millions worldwide, demands early and accurate diagnosis to facilitate timely intervention and improved patient outcomes. This study proposes an innovative approach for dementia classification that harnesses the power of collective intelligence through a voting-based methodology. Proposed method combines machine learning algorithms with the wisdom of the crowd by aggregating predictions from both experts and non-experts. By leveraging the diverse perspectives and expertise of the collective, the system achieves heightened accuracy and robustness in dementia classification. This study presents extensive experimental results demonstrating the effectiveness of this approach and showcase its potential to revolutionize medical decision-making, offering a promising avenue for advancing dementia diagnosis and personalized patient care. The findings unveil the untapped potential of collective intelligence in the healthcare domain and encourage further exploration of collaborative models in medical research and diagnostics.

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