Evaluating the Impact of Imbalanced Data on Malaria Prediction Accuracy

Malaria remains a significant global health challenge, particularly in tropical and subtropical regions. Traditional methods of malaria prediction rely on historical data and basic statistical analysis, which often lack the accuracy needed for effective disease control. In recent years, machine learning (ML) techniques have emerged as powerful tools for malaria prediction, offering improved accuracy and reliability. This study evaluates the performance of different ML models including Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), and Logistic Regression (LR)—for malaria disease prediction. The dataset used consists of microscopic blood sample images categorized into parasite-infected and uninfected samples. Given the imbalance in the dataset, three data balancing techniques—oversampling, undersampling, and data augmentation—were applied to enhance model performance. A comparative analysis of the models was conducted using key performance metrics, including accuracy, precision, recall, F1-score, and ROC-AUC. The results indicate that Random Forest with undersampling achieved the highest accuracy (79.07%) and ROC-AUC (90.24%), making it the most effective model. While oversampling and data augmentation improved recall, they did not significantly enhance overall performance. SVM and Logistic Regression demonstrated stable performance but lagged behind Random Forest, whereas KNN exhibited high recall (97.50%) but suffered from low accuracy due to excessive false positives. The findings suggest that undersampling, particularly with Random Forest, is the most effective approach for malaria prediction in imbalanced datasets. This study highlights the potential of machine learning in enhancing malaria diagnosis and resource allocation, offering valuable insights for disease control strategies.