Optimasi Algoritma KNN Menggunakan Metode PSO dalam Klasifikasi Kanker Paru-Paru

Abstract

Lung cancer remains one of the leading causes of cancer-related mortality worldwide, with early detection being critical for improving patient outcomes. However, conventional diagnostic methods often require substantial time and resources. This study implements and evaluates the integration of Particle Swarm Optimization (PSO) with the K-Nearest Neighbors (KNN) algorithm for lung cancer risk classification using a lung cancer dataset consisting of 20,000 samples and 16 predictive features. The study addresses KNN's limitation in handling irrelevant or redundant features, which can reduce classification accuracy. PSO, a population-based optimization algorithm inspired by the social behavior of bird flocks, is employed to perform feature selection, identifying the most relevant subset of features to enhance model performance. The results show that PSO successfully reduces the number of features from 16 to 4 with improve accuracy of 87,61%, over the baseline KNN model. This reduction improves computational efficiency and facilitates model interpretability without compromising performance, supporting the application of KNN-PSO as a decision support system for early lung cancer detection in clinical settings.