Prediction Of Cancer Using Ensemble Approach Of Machine Learning Algorithms

Abstract

Predicting the disease at early stage and diagnosing it is major need of the society. Due to the cancer the death rate increases day to day. There are so many means through which there is possibility of caner occurrence. These factors include environmental considerations, food habits and regular day to day activities. Developing an automated systems will bring more benefits to human beings by saving them entering into critical situations. In this paper, we propose a novel ensemble method which includes integrating the algorithm to provide best efficient results. The Apriori algorithm generates rules which are then given as input into Eclat. The generated rules are estimated with Magnum Opus which gives out the final set of factors. These are analyzed using Naïve Bayes Algorithm and accuracy is found to be 96% which seems to be good compared to other existing techniques.