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- W4381682021 abstract "Although precision classification is a vital issue for therapy, cancer diagnosis has been shown to have serious constraints. In this paper, we proposed a deep learning model based on gene expression data to perform a pan-cancer classification on 16 cancer types. We used principal component analysis (PCA) to decrease data dimensionality before building a neural network model for pan-cancer prediction. The performance of accuracy was monitored and optimized using the Adam algorithm. We compared the results of the model with a random forest classifier and XGBoost. The results show that the neural network model and random forest achieve high and similar classification performance (neural network mean accuracy: 0.84; random forest mean accuracy: 0.86; XGBoost mean accuracy: 0.90). Thus, we suggest future studies of neural network, random forest and XGBoost models for the detection of cancer in order to identify early treatment approaches to enhance cancer survival." @default.
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- W4381682021 date "2023-06-21" @default.
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- W4381682021 title "Pan-Cancer Classification of Gene Expression Data Based on Artificial Neural Network Model" @default.
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- W4381682021 doi "https://doi.org/10.3390/app13137355" @default.
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