# Pan-Cancer Classification of Gene Expression Data Based on Artificial Neural Network Model
###### 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.  
pan-cancer; gene expression; neural network +
Footnote †: journal: Medical  
The enormous quantities of data obtained from high-throughput technologies available in public repositories such as The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) are used for machine learning algorithms, including artificial neural networks [10; 11]. However, these datasets present some limitations. They consist of a huge number of gene expression levels or clinical data, as well as noise. Often, the ratio of two classes to be predicted (e.g., normal and cancer) is not balanced, causing biased models with lower performances. The number of genes is usually much greater than the number of samples, requiring a reduction of features dimensionality [12]. As gene expression profiles have high dimensionality, machine learning models often require significant time and resources to train and make a prediction.  
Among the wide range of methods applied to reduce data dimensionality and noise, there is principal component analysis (PCA). PCA generates new features, creating a new space from the original feature vectors via a linear transformation [13]. PCA is applied in numerous applications across different fields from social science to biology [14]. Many supervised and unsupervised machine learning algorithms have been applied to gene expression data for cancer prediction [15].  
In the last decade, artificial neural networks (ANNs) have been proposed to deal with huge quantities of data. They are a set of algorithms that mimic the human brain to identify complex associations between features and to generate predictive models [16].  
Here, we investigated a computational method to classify different cancer types based on ANNs. We compared the performance of the ANN model with the random forest classifier and XGBoost algorithm. XGBoost has been shown to obtain good performance in many application fields including chronic kidney disease and epilepsy diagnosis [17; 18]. Several studies have shown that XGBoost obtained better performance in survival prediction in non-small-cell lung cancer and for predicting tissues of origin for 10 different cancer types than other standard machine learning algorithms [19; 20]  
The results of our study might help in the diagnosis and contribute to planning therapies that could improve cancer survival.