An expert clinical decision support system to predict disease using classification techniques

Currently in the healthcare industry different data mining methods are used to mine the interesting pattern of diseases using the statistical medical data with the help of different machine learning techniques. The conventional disease diagnosis system uses the perception and experience of doctor without using the complex clinical data. The proposed system assists doctor to predict disease correctly and the prediction makes patients and medical insurance providers benefited. This research focuses on to diagnosis diabetes disease as it is a great threat to human life worldwide. The system uses the Decision Tree and K-Nearest Neighbor (KNN) Algorithms as supervised classification model. Finally, the proposed system calculates and compares the accuracy of C4.5 and KNN and the experimental result demonstrates that the C4.5 provides better accuracy for diagnosis diabetes. For the clinical database, the Pima Indians Dataset is used in this research.