Cervical cancer prediction using stacked ensemble algorithm with SMOTE and RFERF

With the tremendous advancement in machine learning and deep learning, organizations are using numerous algorithms for analyzing the huge amount of data to come up with insights which contains meaningful out comes. Especially in medical health care systems, machine learning is being used widely to forecast and treat the illness at early in stage. Cervical cancer is one of such diseases which can be diagnosed if identified in early stage, but the symptom-less nature of the disease has become challenging for the researchers and practitioners to predict the disease at early stage. There are numerous factors which will prone to cervical cancer risk in women. In this study we have proposed a stacked ensemble technique which takes heterogenous base learners and a meta learner for predicting the cervical cancer from various risk factors, SMOTE has been used for data Balancing and RFE with Random Forest used for feature extraction through which we have got better accuracy than the existing methods. We have also identified top 8 features which impact the advancement of the classification model. © 2021