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<p>Robust classification of neurodegenerative diseases, such as Alzheimer’s Disease (AD) at the Mild Cognitive Impairment (MCI) stage, requires machine learning models resilient to common data complexities. In this paper, a new distribution-agnostic classification method called the Class Median Min–Max Threshold (CM3T) is presented. CM3T applies a modality-specific threshold and aggregates decisions using a majority voter classifier using volumetric characteristics of brain tissues from 220 MRI samples. Its core computational novelty is deriving decision boundaries from class-specific medians and standard deviations, offering inherent robustness against skewed distributions and class imbalance. Furthermore, CM3T allows independent control of margins for each classification group, an advantage over traditional techniques. The CM3T model significantly outperforms conventional machine learning methods, achieving superior performance with 96.3% accuracy, 97.9% precision, 98% specificity, 94.7% sensitivity, and an F1 score of 96.3%.</p>

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Class median min–max threshold technique for the identification of mild cognitive impairment

  • 期刊论文

  • 发表日期：

    2026年6月3日

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  • Soumyajit Biswas,
  • Subhabrata Roy,
  • Abhijit Chandra

摘要

Robust classification of neurodegenerative diseases, such as Alzheimer’s Disease (AD) at the Mild Cognitive Impairment (MCI) stage, requires machine learning models resilient to common data complexities. In this paper, a new distribution-agnostic classification method called the Class Median Min–Max Threshold (CM3T) is presented. CM3T applies a modality-specific threshold and aggregates decisions using a majority voter classifier using volumetric characteristics of brain tissues from 220 MRI samples. Its core computational novelty is deriving decision boundaries from class-specific medians and standard deviations, offering inherent robustness against skewed distributions and class imbalance. Furthermore, CM3T allows independent control of margins for each classification group, an advantage over traditional techniques. The CM3T model significantly outperforms conventional machine learning methods, achieving superior performance with 96.3% accuracy, 97.9% precision, 98% specificity, 94.7% sensitivity, and an F1 score of 96.3%.