Momentum Backpropagation Optimization for Cancer Detection Based on DNA Microarray Data

International Journal of artificial intelligence research

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Title Momentum Backpropagation Optimization for Cancer Detection Based on DNA Microarray Data
Creator Wisesty, Untari Novia
Sthevanie, Febryanti
Rismala, Rita
Momentum Backpropagation with Adaptive Learning Rate Neural Network Genetic Algorithm DNA Microarray Early Cancer Detection
Description Early detection of cancer can increase the success of treatment in patients with cancer. In the latest research, cancer can be detected through DNA Microarrays. Someone who suffers from cancer will experience changes in the value of certain gene expression.  In previous studies, the Genetic Algorithm as a feature selection method and the Momentum Backpropagation algorithm as a classification method provide a fairly high classification performance, but the Momentum Backpropagation algorithm still has a low convergence rate because the learning rate used is still static. The low convergence rate makes the training process need more time to converge. Therefore, in this research an optimization of the Momentum Backpropagation algorithm is done by adding an adaptive learning rate scheme. The proposed scheme is proven to reduce the number of epochs needed in the training process from 390 epochs to 76 epochs compared to the Momentum Backpropagation algorithm. The proposed scheme can gain high accuracy of 90.51% for Colon Tumor data, and 100% for Leukemia, Lung Cancer, and Ovarian Cancer data.
Publisher STMIK Dharma Wacana
Date 2021-01-05
Type info:eu-repo/semantics/article
Peer-reviewed Article
Format application/pdf
Source International Journal of Artificial Intelligence Research; Vol 4, No 2 (2020): December; 127 - 134
Language eng
Rights Copyright (c) 2021 International Journal of Artificial Intelligence Research

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