In health sciences, prescribing drugs for the patients depends on the diagnosis conducted by the doctors or experts. As the world is moving toward the automation of diagnosis using computers, the prediction of diseases in the human can be automated by using machine learning algorithms which perform the task of classification/ prediction when given the patient details. To predict the patient diseases, a set of historical data is passed to the machine learning algorithms and the algorithm is trained to learn for prediction. Among several machine algorithms, decision tree is the prominent technique to understand how the decision is taken as it represents the classification knowledge in hierarchical form. As decision trees take crisp decision, it is not possible to handle fuzziness which is common in real world data, so fuzzy decision trees emerged. In this paper, we present an empirical study of fuzzy decision trees (FDTs) towards the classification of several medical datasets so as to facilitate an intelligent system to predict the disease and further it can lead to an option of automatic prescription of drugs for the patients as per the predictions made by the machine learning algorithm which can later be verified by the experts if desired. © 2016, International Journal Of Pharmacy and Technology. All rights reserved.