Cervical Cancer Prediction Empowered with Federated Machine Learning

Abstract

Cervical cancer is an intrusive cancer that imitates various women around the world. Cervical cancer ranks in the fourth position because of the leading death cause in its premature stages. The cervix which is the lower end of the vagina that connects the uterus and vagina forms a cancerous tumor very slowly. This pre-mature cancerous tumor in the cervix is deadly if it cannot be detected in the early stages. So, in this delineated study, the proposed approach uses federated machine learning with numerous machine learning solvers for the prediction of cervical cancer to train the weights with varying neurons empowered fuzzed techniques to align the neurons, Internet of Medical Things (IoMT) to fetch data and blockchain technology for data privacy and models protection from hazardous attacks. The proposed approach achieves the highest cervical cancer prediction accuracy of 99.26% and a 0.74% misprediction rate. So, the proposed approach shows the best prediction results of cervical cancer in its early stages with the help of patient clinical records, and all medical professionals will get beneficial diagnosing approaches from this study and detect cervical cancer in its early stages which reduce the overall death ratio of women due to cervical cancer.