LIVER DISEASE PREDICTION BASED ON GRID SEARCH AND RANDOM FOREST CLASSIFICATION

Abstract

The Medical field is “data rich” and “knowledge poor”. This research proposes a Clinical Decision Support System to process this data and early diagnosis of some physiological conditions. With the help of various Machine Learning Techniques we ought to design a CDSS that will assist the doctor to predict disease correctly and thus it may be helpful for patients. This system focuses on to diagnosis of the Liver Diseases. The proposed System uses Decision Tree, Random Forest, Naïve bayes and Support Vector Machine Algorithms for Classification. Finally the proposed system calculates and compares the accuracy of all the four models and demonstrates the best accuracy model for diagnosis of Liver related Diseases.