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Framework to predict health diseases using attribute selection mechanism

This paper displays a structure of the work exercised through the DM methods which helps in the prediction and treatment of various diseases. This examines two problems (elongated work based on prediction of RCT and Heart Disease) with classification technique based on cross validation, decision tree to discover RCT, split validation and model to detect heart disease before probing advice from the doctor. The important aim of this paper is to treat whether there is a need of RCT or not with different input values of an attributes. All these symptoms or attributes are examined to arrive at a decision that person with an age greater than eighteen and having sensitivity issues have greater probability of root canal treatment. There are more chances of patient being prone to a heart problem through attributes like age, root canal treatment, diabetes and smoking. The introduced system comprise the union of computer-based patient records with clinical decision support can lower down medical mistakes, unwanted practice variation and helps in making efficient patient safety and outcome.