Misdiagnosis or late diagnosis of diseases is currently at the forefront of medical errors. These errors not only lead to the death or disability of a significant number of patients around the world each year [1], but also have an indirect impact on the professional life of doctors and the quality of services provided by them. Furthermore, the complaints raised as a result of such errors have always been one of the most important stressors for doctors [2]. Data mining in the medical industry to diagnose and predict diseases has become one of the fields of interest for researchers in recent years [3]. Our aim in this paper is to use data mining methods to find knowledge in a dataset of medical prescriptions provided by the www.Drugs.com site. By analyzing the drugs prescribed for each disease, our proposed method aims to predict the category of each disease and the type of disease that the patient suffers from. In general, the results of this research can be used by, for example, the medical staff of a hospital, including the specialists and general practitioners to reduce the misdiagnosis error rate and also have more confidence in the correctness of the disease diagnosis and the prescribed medication. At the same time, the patients can have more trust in the correct diagnosis of their disease and the prescribed drugs. In addition, it can reduce the cost of compensating for medical errors which could, in turn, lead to lowering the expenses of medical insurance companies. Finally, these results can be used as a basis for future research in this field. Our collected dataset includes details such as the name of the disease, the category of the disease, and the names of the drugs prescribed for each patient. To label the cases, a team of doctors has identified each patient’s disease. A number of experiments have been conducted to compare the performance of different data mining techniques for disease prediction, and the results show that the proposed stacking model is more accurate than other data mining techniques such as Nave Bayes.