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A multi-expert ensemble system for predicting Alzheimer transition using clinical features.

Mario Merone1, Sebastian Luca D'Addario2,3,4, Pierandrea Mirino2,3,5

  • 1Unit of Computer Systems and Bioinformatics, Department of Engineering, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128, Rome, Italy.

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Summary

This study introduces a machine learning tool to predict Alzheimer's disease (AD) risk up to nine years early using simple clinical features. Early detection via this accessible method aids timely intervention and care planning.

Keywords:
ADAS scoreCerebellar impairmentClinical Dementia Rating ScaleEarly diagnosisMachine learningRenal and genitourinary dysfunctions

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Area of Science:

  • Neurology
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Current Alzheimer's disease (AD) diagnosis relies on invasive, costly methods, often detecting the disease after significant brain damage.
  • Early identification of individuals at high risk for AD is crucial for effective management and treatment planning.

Purpose of the Study:

  • To develop and validate an accessible, low-cost machine learning algorithm for predicting AD development.
  • To enable early risk identification years before overt symptoms manifest, facilitating a critical window for intervention.

Main Methods:

  • An ensemble-based machine learning algorithm was developed to predict AD development within nine years.
  • The algorithm utilizes five easily detectable clinical features from neuropsychological tests.
  • Validation was performed on the ADNI open dataset, including healthy individuals and mild cognitive impairment (MCI) patients.

Main Results:

  • The proposed algorithm demonstrated superior balanced accuracy, negative predictive value, and specificity compared to existing solutions.
  • The system effectively predicted AD development in a mixed cohort, unlike prior studies focusing solely on MCI.

Conclusions:

  • The developed machine learning tool offers a promising, accessible, and low-cost approach for early AD risk screening.
  • This tool can be integrated into routine healthcare screenings to support preventive strategies and timely patient care.