Scholarlink

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that significantly affects cognitive function. Early and accurate diagnosis is crucial for timely intervention, yet traditional diagnostic methods can be costly and invasive. This study explores a machine learning (ML) approach leveraging the Bio-Hermes dataset, which integrates blood-derived proteomics, neuropsychological test results, and demographic variables to improve the classification of individuals into cognitively normal (CN), mild cognitive impairment (MCI), and AD categories. We implement four ML models: Elastic Net, Classification and Regression Trees (CART), Random Forest, and Extreme Gradient Boosting (XGBoost). The results indicate that XGBoost achieves the highest accuracy (0.81) and a strong area under the receiver operating characteristic curve (AUCROC) (0.94), highlighting the potential of multimodal ML models in dementia classification. Additionally, the Mini-Mental State Exam (MMSE) and the proteomic marker pTau181 emerged as key predictive variables. This study underscores the feasibility of using blood-based proteomics in conjunction with cognitive assessments for early AD detection and advocates for further validation on larger, diverse cohorts.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Proteomics, Neuropsychological and Demographics Multimodal Machine Learning Approach to Alzheimer’s Disease Prediction on the Bio-Hermes Study Cohort

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Henry Musto,
  • Daniel Stamate,
  • David Reeves,
  • Catharine Morgan,
  • Roxana Hutanu,
  • Kalliopi Mavromati,
  • Dorina Cadar,
  • Daniel Stahl

摘要

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that significantly affects cognitive function. Early and accurate diagnosis is crucial for timely intervention, yet traditional diagnostic methods can be costly and invasive. This study explores a machine learning (ML) approach leveraging the Bio-Hermes dataset, which integrates blood-derived proteomics, neuropsychological test results, and demographic variables to improve the classification of individuals into cognitively normal (CN), mild cognitive impairment (MCI), and AD categories. We implement four ML models: Elastic Net, Classification and Regression Trees (CART), Random Forest, and Extreme Gradient Boosting (XGBoost). The results indicate that XGBoost achieves the highest accuracy (0.81) and a strong area under the receiver operating characteristic curve (AUCROC) (0.94), highlighting the potential of multimodal ML models in dementia classification. Additionally, the Mini-Mental State Exam (MMSE) and the proteomic marker pTau181 emerged as key predictive variables. This study underscores the feasibility of using blood-based proteomics in conjunction with cognitive assessments for early AD detection and advocates for further validation on larger, diverse cohorts.