Ovarain Cancer Prediction in Early Stage Using Machine Learning Approaches (International Journal of Food and Nutritional Sciences, 2022)
Ovarian cancer is a disorder of ovarian cell growth that is triggered by series of acquiredmutations affecting a single cell or its clonal progeny. It is purposeless prey on host andvirtually autonomous. It is usually diagnosed at a late stage because of poor sensitivity ofscreening test. There are still no effective cures for this illness. Still early detection mightlower the mortality rate. Our project's major goal is to conduct predictive analytics for earlydetection by using machine learning models and statistical techniques on clinical datacollected from specific patients. Mutual information testing is crucial in statistical analysisfor identifying indicative biomarkers. A collection of machine learning models, such as theRandom Forest (RF), Extreme Gradient Boosting Machine (XGBoost), Logistic Regression(LR), Gradient Boosting Machine (GBM), and Light Gradient Boosting Machine (LGBM)areutilized in the classificationof ovarian tumors as benign or malignant. By using proposedsystem, it can significantly identify the class of benign and malignant patients. The datacollected is analyzed and pre-processed before it is used for model training and testing.