Cervical Cancer Prediction Using Machine Learning Algorithms

Abstract

Cervical cancer is a disease of the cervix, a variety of Human Papilloma Virus (HPV), a highly contagious disease, which plays a role in causing harmful cervical growth. The cervix is the lower part of the uterus that is connected to the vagina. For the majority of women, the unaffected frame prevents it but in small amounts, the infection persists for a long time. The definition of general pass due to compulsory access to cervical screening techniques is a rare test. Visible evidence of this infection can be successfully treated. Testing tests include Pap tests and HPV DNA tests. Relevant information is available and includes patient duration, smokers or not, hormonal contraceptives, use of Intra Uterine Device (IUD), sexually transmitted infections (STDs) and more. This work focuses on predicting the harmful growth of the cervix for HVP testing and Pap testing using AI statistics such as Vector Support Machine (SVM), strategic calculation, direct backlash and optional tree