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.
Full text not available from this repository.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) |
|---|---|
| Keywords: | Alzheimer; LORENS; accuracy; training; Bayes |
| Subjects: | 000 Computer Science, Information and General Works > 000 Computer Science, Knowledge and Systems > 005 Computer Programming |
| Divisions: | Universitas Multimedia Nusantara |
| Depositing User: | Administrator UMN Library |
| Date Deposited: | 01 Mar 2018 04:06 |
| Last Modified: | 11 Jan 2023 06:25 |
| URI: | https://kc.umn.ac.id/id/eprint/2778 |
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