A new approach for liver disease detection using KNN and Back propagat

Heavy consumption of hybrid food in today’s world causes for rising of different diseases. So the study of this medical diagnosis becomes the most important part of disciplines. If there is no proper knowledge of disease then it causes serious effects. Therefore there is a requirement of strong diagnosis system. This is made possible by K-nearest neighbor algorithm and Back propagation neural network. K- Nearest algorithm is based on non-parameterized family used for regression and classification. Back propagation neural network is another technique used for diagnosis of disease based on artificial neural network used for optimization method. In this Work the comparison of both algorithms is presented and how this technique has combinable produced the better result is discussed. The combined approach provides better accuracy up to 96%.