The healthcare environment is still ‘information rich’ But ‘knowledge poor’. There is a wealth of data available within the health care systems. However, there is a lack of effective analysis tools to discover hidden relationships in data. The aim of this work is to design a GUI based Interface to enter the patient symptoms and predict which disease the patient is having using various machine learning algorithms. The prediction is performed from mining the patient’s symptom data or data repository. This paper has analyzed prediction systems for disease using more number of input attributes. The system uses medical terms such as fever, pain, cholesterol-like attributes to predict the likelihood of a patient getting a particular disease. Until now, over 100 attributes are used for prediction. The data mining classification techniques, namely Decision Trees, Naive Bayes, and Random Forest are analyzed on disease database. The performance of these techniques is compared, based on accuracy.