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

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 combin...

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Bibliographic Details
Published in:Progress in artificial intelligence 2024-12, Vol.13 (4), p.367-379
Main Authors: Tayyab, Muhammad Arabi, Alim, Affan, Alam, Mansoor, Su’ud, Mazliham Mohd
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Blending
Blood
Classification
Computational Intelligence
Computer Imaging
Computer Science
Control
Data Mining and Knowledge Discovery
Datasets
Ensemble learning
Image enhancement
Machine learning
Malaria
Mechatronics
Medical imaging
Natural Language Processing (NLP)
Pattern Recognition and Graphics
Regular Paper
Robotics
Vision
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Summary: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.
ISSN:2192-6352
2192-6360
DOI:10.1007/s13748-024-00346-9