A Smart Clinical Decision Support System to Predict diabetes Disease Using Classification Techniques

Abstract

In the present living scenario a diseases are one of the crucial reason for the increasing death rates. The major problem is inaccurate identification of diseases which give rise to a complex task which needs to be evaluated specifically and efficiently with a desirable automated technology. Different human beings have different skills, so as doctors. Due to the lack of specialized doctors in remote areas the health services are not availed easily.as in our presented system there are several classification techniques available that can be used for clinical decision support system different techniques are used for different diagnosis.As our proposed system uses the Decision Tree and K-Nearest Neighbor (KNN) Algorithms as supervised classification model for diabetes disease because it is a great threat to human life worldwide finally our proposed system will reduce the time and cost of diagnoses.