Prediction of disease based on prescription using data mining methods
The volume of data being gathered every day is large and health care societies correspondingly generate a large volume of information daily. Although health care industry is rich in information but it requires discovering concealed relationships and patterns in data. The aim of this paper is employing data mining methods to find out knowledge in a dataset that was provided by a research center. By analyzing the drugs that were bought by each patient, our proposed method aims to predict the type of physician each patient has referred to and the type of disease he is suffering from. Our collected dataset contains details such as sex, age and the names of the drugs prescribed for each patient. For labeling the instances, a group of pharmacy students and professors has determined each patient’s disease. A number of experiments have been performed to compare the performance of different data mining techniques for predicting the diseases and the results illustrate that the proposed Stacking Model has higher accuracy compared to other data mining techniques such as k-Nearest Neighbor (kNN).
KeywordsData mining Predicting disease Stacking learning algorithm
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
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