Abstract
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.
Author Contributions: SP, AS, and RC conceived the concepts, planned, and designed the article. SP, AS, and RC primarily wrote and edited the manuscript.
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The authors declare that they have no competing interests.
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No external funding has been utilized for this study.
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Expression_Matrix.csv: Gene expression matrix for selected 14 genes.
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Chopade, R., Stanam, A., Pawar, S. (2023). Neural Networks for Predicting Severity of Ovarian Carcinomas. In: Nagar, A.K., Singh Jat, D., Mishra, D.K., Joshi, A. (eds) Intelligent Sustainable Systems. Lecture Notes in Networks and Systems, vol 578. Springer, Singapore. https://doi.org/10.1007/978-981-19-7660-5_7
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