A Comprehensive Study on the Application of Machine Learning Algorithms in the Prognosis of Ovarian Cancer

Ovarian cancer is the third leading type of cancer found in women in India and ranks seventh globally. Several studies have shown that the population affected by ovarian cancer is profound to increase in the future. It is necessary to take steps for identifying cancer at the early stages to avoid mortality and recurrence. This chapter aims to survey the different ways machine learning models have been used in the prognosis of ovarian cancer - to predict the disease progression, recurrence, and mortality rate; analysis of genomic data sets; correlations and pattern analysis, and finding risk factors. The effective analytics on the imaging and other forms of data available from the patient’s electronic health records could unveil the possibilities of better or early diagnosis of ovarian cancer. The chapter will summarize the taxonomy of the various ways in which machine learning helps in ovarian cancer diagnosis, early detection, and treatment. In addition to surveying the current state-of-the-art application of machine learning algorithms for ovarian cancer diagnosis, the chapter aims to provide future research directions.