Neural Networks for Predicting Severity of Ovarian Carcinomas

Among gynecological cancers, ovarian cancer is the greatest cause of death. The lack of efficient early detection measures is linked to diagnosis at an advanced stage and a bad prognosis. Several genes have been shown to have significantly expressed in early and late stages of ovarian cancer. The use of a neural network can help infer meaning and find patterns from large data sets. The benefit of a neural network is that it is adaptive in nature, learning from the information it receives and adjusting its weights for a better prediction in instances when the outcome is unknown. Some studies have shown effective utilization of neural networks with miRNA data in ovarian cancers, others have used it in gastric cancers with survival data sets, and this study focuses on testing them on gene expression data in ovarian cancers. We found a robust RMSE values for prediction, a reasonable k-fold cross validation, and robust cross grade-stage predictions. This study defines a clear scope of utilizing neural networks in predicting grades and stages in ovarian cancers.