Breast Cancer Prediction Using Machine Learning Algorithm with Big Data Concept

Abstract

The evolution in modern computer technology produce an huge amount of data by the way of using updated technology world with the lot and lot of inventions. The algorithms which we used in machine-learning traditionally might not support the concept of big data. Here we have discussed and implemented the solution for the problem, while predicting breast cancer using big data. DNA methylation (DM) as well gene expression (GE) are the two types of data used for the prediction of breast cancer. The main objective is to classify individual data set in the separate manner. To achieve this main objective, we have used a platform Apache Spark. Here,we have applied three types of algorithms used for classification, they are decision tree, random forest algorithm, support vector machine algorithm which will be mentioned as SVM .These three types of algorithm used for producing models used for breast cancer prediction. Analyze have done for finding which algorithm will produce the better result with good accuracy and less error rate. Additionally, the platforms like Weka and Spark are compared, to find which will have the better performance while dealing with the huge data. The obtained outcome have proved that the Support Vector Machine classifier which is scalable might given the better performance than all other classifiers and it have achieved the lowest error range with the highest accuracy using GE data set