An Efficient and Robust Model for Cervical Cancer Risk Classification based on Random Forest Classifier

As per the World Health Organization, cervical cancer is the fourth most common type of cancer that has a high fatality rate. This disease affects women all over the world, particularly in low-income and middle-income nations. Cancer of the cervix is one of the types of cancer that most freq uently strikes women and affects their reproductive organs. It takes place when cells that are normally found in the cervix transform into malignant cells. The human papillomavirus (HPV), which is spread through sexual activity, is the most important risk factor for developing cervical cancer. There is a significant amount of interest in machine learning, and scientists generally examine its application in every possible setting. Using a Random Forest classifier, the primary purpose of this work is to categorize the clinical dataset of cervical cancer to determine the type of cervical cancer test. Because the dataset is unbalanced and is lacking a significant amount of value, it must be going through an intensive data pre-processing phase. The effectiveness of categorization was tested were quantified using confusion matrices. This was done to establish the efficiency power of every categorization technique.