Malaria Detection using Multi Model Algorithms

Malaria is a major global health concern, services, novel for malaria detection using a combination of multiple machine learning algorithms. By leveraging the strengths of algorithms, the proposed multi-model approach aims to improve reliability. The proposed method first preprocesses the dataset to extract relevant features and then apply different machine learning to classify malaria-infected and uninfected blood samples. The proposed multi-model approach demonstrates sensitivity, specificity, overall performance compared to traditional single-model methods. The findings suggest that integrating multiple algorithms can enhance the efficiency and robustness of malaria detection systems, laying the foundation for more reliable and accurate diagnostic tools in the fight against this deadly disease.