Comparative Analysis of Machine Learning Models on the Classification of Pneumonia Disease Using Chest X-ray Images

Abstract

Pneumonia is one of the leading causes of illness and death globally. If not treated promptly, it can be fatal. Early detection of pneumonia significantly reduces mortality rates and improves the chances of recovery. Among the key diagnostic tools for pneumonia is the chest X-ray, which is widely used due to its affordability. However, diagnosing pneumonia based on chest X-ray images can be challenging, as the visual symptoms may resemble those of other respiratory conditions. These diagnostic challenges are often subjective and dependent on the practitioner’s experience. To address this, computer-aided diagnostic (CAD) technologies can assist healthcare professionals in improving diagnostic accuracy. This research proposes a machine learning-based method to classify pneumonia using chest X-ray images. Specifically, it presents a framework that employs Random Forest (RF), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM) models for automated pneumonia detection. This study involves developing and evaluating these models on chest X-ray images resized to 224 × 224 pixels. To address class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied. The results demonstrate high classification accuracy: SVM achieved 97%, KNN 98% and RF 94%. These outcomes outperform some previously reviewed models and show potential for accelerating early diagnosis and treatment of pneumonia disease.