Title: A Machine Learning Approach towards Early Detection of Ovarian Cancer
 
Abstract: Ovarian cancer poses a great threat to the community due to its high fatality rate and unsatisfactory early stage detection performance. Great effort has been spent in this domain to discover reliable biomarkers for non-invasive screenings, however, the effects so far are marginal. Machine learning methods provide opportunities to deal with difficulties, such as high variable dimensionality and small sample sizes, that are common in the field. Therefore, this research provides a machine learning approach to improve the classification ability and explainability in this process to discover more reliable biomarkers.