TY - GEN
T1 - Feature selection for the classification of Alzheimer's disease data
AU - Alashwal, Hany
AU - Abdalla, Areeg
AU - El Halaby, Mohamed
AU - Moustafa, Ahmed A.
N1 - Funding Information:
This work received financial support from the United Arab Emirates University (grant no. CIT 31T085).
Publisher Copyright:
© 2020 Association for Computing Machinery.
PY - 2020/1/12
Y1 - 2020/1/12
N2 - In this paper, we describe the features of our large dataset (6400+ rows and 400+ features) that includes Alzheimer's disease (AD) patients, individuals with mild cognitive impairment (MCI, prodromal stage of Alzheimer's disease), and healthy individuals (without AD or MCI). We also, present a feature selection method applied on the dataset. Unlike prior data mining models that were applied to AD, our dataset is big in nature and includes genetic, neural, nutritional, and cognitive measures of all the individuals. All of these measures in the data have been shown by empirical studies to be related to the development of AD. We used a random forest classifier to discover which features best classify and differentiate between AD patients and healthy individuals. Identifying these features will likely provide evidence for protective factors against the development of AD.
AB - In this paper, we describe the features of our large dataset (6400+ rows and 400+ features) that includes Alzheimer's disease (AD) patients, individuals with mild cognitive impairment (MCI, prodromal stage of Alzheimer's disease), and healthy individuals (without AD or MCI). We also, present a feature selection method applied on the dataset. Unlike prior data mining models that were applied to AD, our dataset is big in nature and includes genetic, neural, nutritional, and cognitive measures of all the individuals. All of these measures in the data have been shown by empirical studies to be related to the development of AD. We used a random forest classifier to discover which features best classify and differentiate between AD patients and healthy individuals. Identifying these features will likely provide evidence for protective factors against the development of AD.
UR - http://www.scopus.com/inward/record.url?scp=85082020585&partnerID=8YFLogxK
U2 - 10.1145/3378936.3378982
DO - 10.1145/3378936.3378982
M3 - Conference contribution
AN - SCOPUS:85082020585
T3 - ACM International Conference Proceeding Series
SP - 41
EP - 45
BT - Proceedings of the 2020 3rd International Conference on Software Engineering and Information Management, ICSIM 2020 - Workshop 2020 the 3rd International Conference on Big Data and Smart Computing, ICBDSC 2020
PB - Association for Computing Machinery (ACM)
T2 - 3rd International Conference on Software Engineering and Information Management, ICSIM 2020 - and its Workshop 2020 the 3rd International Conference on Big Data and Smart Computing, ICBDSC 2020
Y2 - 12 January 2020 through 15 January 2020
ER -