Developing a decision support system using different classification algorithms for polyclinic selection

A significant part of the patients applying to the emergency department in Turkey are green triage patients. Green triage means patients keep the emergency department unnecessarily busy. This situation causes inefficient use of health services and unnecessary density in the emergency department. This study aims to create a decision support system that allows patients to be directed to the right polyclinic using text-mining techniques and a large language model (LLM). The study sample consists of medical records of patients who came to the emergency department within a year. The study was carried out in two steps: association analysis and classification analysis. Zemberek Natural Language Library was used for root analysis of the words in the data set. 32 association rules were obtained from the data with the Apriori algorithm. Classification analysis was performed for word-polyclinic matching according to association analysis rules. Of the classification algorithms used decision tree, k-nearest neighbors (K-NN), support vector machines (SVM), and random forest. Accuracy rates were obtained as 81.3 %, 79.6 %, 83.4 % and 83.1 %, respectively. Additionally, the classification was performed using ChatGPT from LLMs. Polyclinic classification made with ChatGPT was found 78.9 % accuracy rate. All classical machine learning algorithms showed higher accuracy than ChatGPT. However, when ChatGPT’s Cohen’s kappa (0.798) and F-measure (0.813) values are examined, it can be said that it is similar to the Random Forest algorithm and the SVM algorithm. Nevertheless, the highest accuracy rate belongs to the SVM algorithm. This study shows that the SVM algorithm can classify patients on a polyclinic basis according to their complaints and that an effective decision support system that helps guide patients can be created.