A Machine Learning Approach to Prostate Cancer Risk Classification Through Use of RNA Sequencing Data

Advancements in RNA sequencing technology have made genomic data acquired during sequencing more precise, making models fitted to sequencing data more practical. Previous studies conducted regarding prostate cancer diagnosis have been limited to microarray data, with limited successes. We utilized The Cancer Genome Atlas’ (TCGA) prostate cancer sequencing data to test the viability of fitting machine learning models to RNA sequencing data. A major challenge associated with the sequencing data is its high dimensionality. In this research, we addressed two complementary tasks. The first was to identify genes most associated with potential cancer. We started by using the mutual information metric to identify the most significant genes. Furthermore, we applied the Recursive Feature Elimination (RFE) algorithm to reduce the number of genes needed to identify cancer. The second task was to create a classification model to separate potential high-risk patients from the healthy ones. For the second task, we combated the high dimensionality challenge with Principal Component Analysis (PCA). In addition to high dimensionality, another challenge is the imbalanced data set that has a 10:1 class imbalance of cancerous and healthy tissue respectively. To combat this problem, we used the Synthetic Minority Oversampling Technique (SMOTE) to create synthetic observations and equalize the class distribution. We trained and tested a logistic regression model using 5-fold cross-validation. The results were promising, significantly reducing the false negative rate as compared to current diagnostic techniques while still keeping the false positive rate low. The model showed great improvements over previous machine learning attempts to diagnose prostate cancer. Our model could be applied as part of the patient diagnosis pipeline, helping to improve accuracy.