Detection of cervical cancer using Ensemble Approach in Machine Learning

In the recent years most common gynecologic malignancies worldwide is still cervical cancer. Early screening is the most efficient method of reducing the worldwide burden of cervical cancer because it is a disease that is highly preventable. However, due to limited knowledge and access in developing nations, the vulnerable patient must go to medical facilities and undergo very costly operations. The majority of people cannot afford to be checked out frequently. An ensemble strategy is offered to assess the possibility of cervical cancer in this paper. The ensemble techniques like bagging including random forest, boosting including Adaptive Boosting and Extreme Gradient Boosting, Light gradient boosting machine, and other effective algorithms such as Decision tree and Support Vector Machine are showcased. This method is used to overcome the challenges that previous cervical cancer studies encountered. To improve performance, a method for data rectification is proposed. Since the ensemble strategy performs better than single classifier, this proposed approach is more scalable and useful than others.