BwMMV-pred: a novel ensemble learning approach using blood smear images for malaria prediction

Abstract The use of machine learning in healthcare has become widespread, enhancing the capabilities of doctors and clinicians. This study introduces a novel ensemble learning approach named Blending with Meta Majority Voting (BwMMV) for malaria prediction using blood smear images. The BwMMV technique combines the strengths of eight base classifiers to form an intermediate dataset, which is subsequently used to train five distinct meta-models using different machine learning algorithms. A Local Binary Pattern Histogram (LBPH) method is employed to extract texture features from blood smear images, effectively capturing the underlying patterns necessary for classification. The final classification decision is determined through a majority voting mechanism, selecting the outcome with the most votes as the final prediction. Our results indicate that the BwMMV approach significantly outperforms traditional hard voting and blending techniques, achieving superior accuracy, robustness, and resilience in performance. This innovative method demonstrates promising potential as a powerful tool for automated diagnosis systems, with the ability to be expanded to analyze various datasets efficiently.