A Proficient System for Automatic Detection of Risk Level in Disease Detection using Association Rule Based DRF Algorithm

A challenging research problem for researchers is predicting heart problem, breast cancer, tumor, and the most daunting diseases. Current research in this area is struggling to provide accurate and better solution for the prediction of such deadly diseases. In this paper, Discriminative Rule Framing (DRF) algorithm is proposed to analyze and predict the survivability of disease in a patient. Association rule of data mining is used to reveal the biological hidden patterns and derive association rules from a huge medical data set. Initial rules generated through association rule mining along with subset attributes of the data set are given as input to the DRF risk analysis system to predict the risk level of a given data set. The significance of the DRF is evaluated using confidence, support and lift metrics. Experimental result shows that, prediction level of the DRF is more accurate than other existing algorithms. Index terms -Association Rule Mining, Feature Matching, Risk Analysis, Convictional Measures, CART, Machine Learning.