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Logistic Regression Ensemble to Classify Alzheimer Gene Expression

Kuswanto, Heri and Werdhana, Reynaldi Wisnu (2017) Logistic Regression Ensemble to Classify Alzheimer Gene Expression. In: Proceedings of 2017 International Conference on Smart Cities, Automation & Intelligent Computing Systems (ICON-SONICS 2017), 08 November 207, Yogyakarta.

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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 Naïve 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 Naïve 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.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Alzheimer; LORENS; accuracy; training; Bayes
Subjects: T Technology > T Technology (General)
T Technology > T Technology (General) > T55 Industrial engineering. Management engineering > T59.5 Automation
Divisions: Universitas Multimedia Nusantara
Depositing User: mr admin umn
Date Deposited: 01 Mar 2018 04:06
Last Modified: 25 Apr 2018 06:26
URI: http://kc.umn.ac.id/id/eprint/2778

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