TY - GEN
T1 - Machine Learning Based Screening Tool for Alzheimer’s Disease via Gut Microbiome
AU - Velasquez, Pedro
AU - Oudah, Mai
N1 - Funding Information:
This research was carried out on the High Performance Computing resources at New York University Abu Dhabi.
Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - As the connection between the gut and brain is further researched, more data has become available, allowing for the utilization of machine learning (ML) in such analysis. In this paper, we explore the relationship between Alzheimer’s disease (AD) and the gut microbiome and how it can be utilized for AD screening. Our main goal is to produce a reliable, noninvasive screening tool for AD. Several ML algorithms are examined separately with and without feature selection/engineering. According to the experimental results, the Naive Bayes (NB) model performs best when trained on a feature set selected by the correlation-based feature selection method, which significantly outperforms the baseline model trained on the original full feature space.
AB - As the connection between the gut and brain is further researched, more data has become available, allowing for the utilization of machine learning (ML) in such analysis. In this paper, we explore the relationship between Alzheimer’s disease (AD) and the gut microbiome and how it can be utilized for AD screening. Our main goal is to produce a reliable, noninvasive screening tool for AD. Several ML algorithms are examined separately with and without feature selection/engineering. According to the experimental results, the Naive Bayes (NB) model performs best when trained on a feature set selected by the correlation-based feature selection method, which significantly outperforms the baseline model trained on the original full feature space.
KW - Alzheimer’s disease
KW - Feature Engineering
KW - Feature Selection
KW - Gut Microbiome
KW - Machine Learning
UR - http://www.scopus.com/inward/record.url?scp=85169011455&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85169011455&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-38079-2_7
DO - 10.1007/978-3-031-38079-2_7
M3 - Conference contribution
AN - SCOPUS:85169011455
SN - 9783031380785
T3 - Lecture Notes in Networks and Systems
SP - 62
EP - 72
BT - Practical Applications of Computational Biology and Bioinformatics, 17th International Conference (PACBB 2023)
A2 - Rocha, Miguel
A2 - Fdez-Riverola, Florentino
A2 - Mohamad, Mohd Saberi
A2 - Gil-González, Ana Belén
PB - Springer Science and Business Media Deutschland GmbH
T2 - 17th International Conference on Practical Applications of Computational Biology and Bioinformatics, PACBB 2023
Y2 - 12 July 2023 through 14 July 2023
ER -