Diagnostic Test classification of cervical cancer abnormalities using Bagging Classifier

Regardless of the illness, a patient has a higher chance of recovery if the illness is caught early rather than later. It seems to reason that if we do not know how to treat patients, whatever treatment we can supply will be helpful and lead to a more pleasant existence for the patient. One such illness is cervical cancer, which ranks number four on the list of the most prevalent cancers among females worldwide. Age and the use of hormonal contraceptives are only two of the numerous risk factors that might lead to cervical cancer. Cervical cancer survival rates can be improved and mortality rates reduced with early identification. The goal of this work is to use machine learning methods towards the creation of a model that can detect cervical cancer with a high degree of specificity and accuracy. With the help of Bagging Classifier, we were able to develop a diagnostic test for cervical cancer. Lastly, we compared our results to those of other research and discovered that, according to several assessment metrics, our models improved upon their predecessors' performance in diagnosing cervical cancer.