Determination of Risk Prediction using Random forest technique for Breast Cancer

Models assume a consistent set of behaviors throughout the crisis, whereas people can change their behaviors in response to the realities of the crisis. In many models, the magnitude of second variable would likely inflate the coefficients for that variable in a regression. The major problem in India is that high-quality healthcare facilities are primarily concentrated in major cities. However, the situation is now changing as more hospitals develop in smaller towns and offer modern cancer care. In this paper, the authors have designed a predictive model for predicting different MAPK proteins from the Heat map that leads to Breast cancer. The random forest technique has been used for the design of the model and the average square error has been evaluated. Lastly, the model was validated by Resubstitution Technique.