Chronic Kidney Disease Prediction using Machine Learning Techniques

Predictive analytics for healthcare using machine learning is a challenging task. Development of a fast and accurate predictive analytics tool can lead to a lot of advancement in healthcare. Early detection of any disease leads to early medication. Our project is determined to solve one such real world problem i.e., early detection of chronic kidney disease, with a good accuracy and at a faster rate compared to the existing manual process. The aim of our project is to see how Machine learning algorithms such as the Support vector machine (SVM), Logistic Regression and K-Nearest Neighbor (KNN) classifiers help us to detect CKD, compare their accuracies and to use the best model among them to build a web application.