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Deep learning-based cancer disease classification using Gene Expression Data

J. Dafni Rose, K. Vijayakumar, D. Menaga

Abstract


Cancer disease caused a major death in worldwide. To prevent this, various cancer classification approaches are employed, which are mostly relied on clinical characteristics and histopathological characteristics. Deep learning-based classification models are very effective and accurate. As a result, this research established the Adam-based Deep Quantum Neural Network, which is the optimal deep learning-based cancer classification method. The information on gene expression is used to classify cancer. Using the Box-Cox transformation, which converts the data into a legible format, the data transformation procedure is carried out. Utilizing information gain, the features are chosen in order to choose the proper gene expression. Additionally, Deep QNN is used to classify cancer, and for better classification, which is trained via Adam optimization. The experimental result shows the developed model provide better classification result with respect to accuracy, true positive rate and true negative rate of 94.91%, 95.59% and 95.4%.


Keywords


cancer classification; Adam optimization; data transformation; deep learning; gene expression

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References


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DOI: https://doi.org/10.32629/jai.v7i1.734

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Copyright (c) 2023 J. Dafni Rose, K. Vijayakumar, D. Menaga

License URL: https://creativecommons.org/licenses/by-nc/4.0/