Performance Evaluation of K-Nearest Neighbors and Random Forest on Monkeypox Dataset Using Particle Swarm Optimization (PSO)

Abstract

This study aims to evaluate and compare the performance of the K-Nearest Neighbors (KNN) and Random Forest (RF) algorithms in classifying Monkeypox disease by applying the Particle Swarm Optimization (PSO) technique. PSO is used to obtain optimal parameters for each algorithm in order to improve the accuracy and efficiency of the classification models. The data used consist of 10,000 patient records with 11 clinical symptom features representing health conditions related to Monkeypox infection and are numerical in nature. Model performance evaluation was conducted before and after optimization using accuracy, precision, recall, F1-score, and sensitivity metrics. The results show that the application of PSO improves the performance of both algorithms compared to the non-optimized models, particularly in enhancing accuracy and balancing precision and recall. Overall, the combination of classification algorithms with the PSO optimization technique produces more optimal performance in detecting Monkeypox disease. This study is expected to contribute to the development of more accurate machine learning based classification systems and to support effective early diagnosis of Monkeypox. GS Cited Analysis