An Optimized Machine learning model for Automatic Prediction of Cervical Cancer Using Decision Tree Classifier

Cervical cancer is the fourth leading cause of death from illness in women worldwide. The human papillomavirus, also known as HPV, has been linked to the development of cervical cancer. The discovery that cervical cancer can be prevented through earlier monitoring has contributed to a reduction in the disease's overall burden around the world. Women in underdeveloped countries do not participate in adequate monitoring programs due to the high expense of the methods required to undertake examinations regularly, low levels of awareness, and restricted access to medical facilities. By proceeding with this method, a very high level of danger is to be anticipated for the specific patient. In this article, we suggested a Decision Tree Classifier to predict cases of cervical cancer. However, in this analysis of cancer data, a total of 32 risk factors are deemed to be related to four different target variables: Hinselmann, Schiller, Cytology, and Biopsy. In comparison to the conventional techniques, the outcomes demonstrated that the composite classification technique was suggested to be effective for the study of cervical cancer.