Disease Detection System

Abstract

In this project I had developed a system based on machine learning that helps to detect a Heart, Kidney and Liver disease of a person. The idea for this project is derived from IEEE papers. The papers suggest Random Forest Classifier for developing such a system. I also referred another IEEE paper which uses Machine Learning to deal with this. Random forest is a Supervised Machine Learning Algorithm that is used widely in Classification and Regression problems. It builds decision trees on different samples and takes their majority vote for classification and average in case of regression. One of the most important features of the Random Forest Algorithm is that it can handle the data set containing continuous variables as in the case of regression and categorical variables as in the case of classification. It performs better results for classification problems. The aim is to develop an application that will detect heart, kidney and liver diseases. In the project, we have to detect whether a person has heart disease or not. Then if the person has heart disease, we provide necessary recommendations. Also, we have to predict whether a person has kidney disease or not. Then if the person has Kidney disease, we provide necessary recommendations. Diet recommendation for patient will be given according to potassium zone which is calculated using blood potassium level to slow down the progression of CKD. There are 3 zones that are defined for kidney disease patients. They are safe zone, caution zone and Danger zone. Similar case in liver diseases prediction. We have to detect whether a person has liver disease or not. If the person has liver disease, necessary recommendations are provided.