@inproceedings{356c821a54a14848aa8a10c92bef098b,
title = "Machine learning models reveal theimportance of clinical biomarkers for the diagnosis of Alzheimer's disease",
abstract = "Alzheimer's Disease (AD) is a neurodegenerative disease that causes complications with thinking capability, memory and behavior. AD is a major public health problem among the elderly in developed and developing countries. With the growth of AD around the world, there is a need to further expand our understanding of the roles different clinical measurements can have in the diagnosis of AD. In this work, we propose a machine learning-based technique to distinguish control subjects with no cognitive impairments, AD subjects, and subjects with mild cognitive impairment (MCI), often seen as precursors of AD. We utilized several machine learning (ML) techniques and found that Gradient Boosting Decision Trees achieved the highest performance above 84% classification accuracy. Also, we determined the importance of the features (clinical biomarkers) contributing to the proposed multi-class classification system. Further investigation on the biomarkers will pave the way to introduce better treatment plan for AD patients.",
keywords = "ADNI, Alzheimer's Disease, Dementia, Mild Cognitive Impairment",
author = "Refaee, {Mahmoud Ahmed} and Ali, {Amal Awadalla Mohamed} and Elfadl, {Asma Hamid} and Abujazar, {Maha F.A.} and Islam, {Mohammad Tariqul} and Kawsar, {Ferdaus Ahmed} and Mowafa Househ and Zubair Shah and Tanvir Alam",
note = "Publisher Copyright: {\textcopyright} 2020 The authors and IOS Press.",
year = "2020",
doi = "10.3233/SHTI200599",
language = "English",
series = "Studies in Health Technology and Informatics",
publisher = "IOS Press",
pages = "478--481",
editor = "John Mantas and Arie Hasman and Househ, {Mowafa S.} and Parisis Gallos and Emmanouil Zoulias",
booktitle = "THE IMPORTANCE OF HEALTH INFORMATICS IN PUBLIC HEALTH DURING A PANDEMIC",
address = "United States",
}