@inproceedings{19acc3038dbf43158c421324a95443d2,
title = "Logistic regression ensemble to classify Alzheimer gene expression",
abstract = "Alzheimer is a degenerative disease and one of the most common cases of dementia. One of the important keys to treat Alzheimer is an early detection which can be done by analyzing the genes expression in the DNA by using DNA microarray technology. The basic problem in classification is to find out the best method which is able to accurately predict the case. This reearch applies Logistic Regression Ensemble (LORENS) to classify Alzheimer related genes and compare the result with Na{\"i}ve Bayes classifier. This research examines 178 observations consisting of 2 classes where 98 observations are Alzheimer's genes and 80 observations are normal genes. The analysis shows that LORENS outperforms the Na{\"i}ve Bayes classifier evaluated with Cross Validation. The best LORENS setting is obtained for 5 partitions and threshold 0.5 which leads to 75.28% accuracy and 0.759 for the Area Under Curve (AUC). This results indicate that LORENS is a good appoach to classify Alzheimer gene expression.",
keywords = "Alzheimer, Bayes, LORENS, accuracy, training",
author = "Heri Kuswanto and Werdhana, {Reynaldi Wisnu}",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 International Conference on Smart Cities, Automation and Intelligent Computing Systems, ICON-SONICS 2017 ; Conference date: 08-11-2017 Through 10-11-2017",
year = "2017",
month = jul,
day = "2",
doi = "10.1109/ICON-SONICS.2017.8267818",
language = "English",
series = "Proceeding of 2017 International Conference on Smart Cities, Automation and Intelligent Computing Systems, ICON-SONICS 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "36--41",
booktitle = "Proceeding of 2017 International Conference on Smart Cities, Automation and Intelligent Computing Systems, ICON-SONICS 2017",
address = "United States",
}