A Two-Phase Classifier Model for Predicting the Drug Satisfaction of the Patients Based on Their Sentiments

ABSTRACT

Since the corona virus has emerged, genuine clinical resources, such as a paucity of experts and healthcare workers, a lack of adequate equipment and medications, and so on, have reached their peak of inaccessibility. Several people have died as a consequence of the medical profession’s concern. Individuals began self-medicating due to a lack of supply, which exacerbated an already precarious health situation. A rise in new ideas for automation is being spurred by machine learning’s recent success in a varied variety of applications. In this paper, we have proposed a two-phase Decision Tree Classifier based on Artificial Neural networks (DTNN). The work is based on the satisfaction of the drugs among patients with the help of their comments as positive or negative polarity. The dataset of drugs used in this paper is Cymablta and Depopovera. The proposed results are compared with the existing methodology of Support Vector Machine Neural Network (SVMNN). The results are shown in graphical and tabular form which shows the efficiency of the proposed methodology. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.