A Machine Learning based Decision Support System to Predict the Presence of Cervical Cancer

Cancer occurring in cervix, a part connecting the uterus and vagina, is one of the frequently happening cancer types among gynecological cancers following uterine cancer. The symptoms generally are experienced once the patient progresses to stage 1b cervical carcinoma on the canal (cervix uteri) joining the uterus and vagina which is clinically observable lesion. The Pap-Smear test is used for early-stage detection technique. Identification of the precancerous or cancerous cells on the individual’s cervix is done by the Papanicolaou test. Pap-Smear produces false-positive/false-negative results. Machine learning algorithms could provide us with an accurate diagnosis by conducting classification, prediction, and estimation. The ensemble technique incorporates four algorithms using the concept of machine learning. The latter provides the highest accuracy of 99% so that it enables the healthcare practitioners to make informed decisions. The proposed work aims to develop an intelligent decision support system which can aid medical practitioners in carrying out informed medical procedures. The system when containerized could make way for portability and easy deployment.