A Comprehensive Review of Machine Learning Techniques for Malaria Diagnosis from Blood Samples

This study paper provides an overview of the use of machine learning techniques for malaria diagnosis. The article looks at a variety of machine learning algorithms, such as supervised and unsupervised learning, deep learning techniques, and hybrid approaches, and how they might be used to diagnose malaria. In addition, widely used data sets and preprocessing methods for detecting malaria are discussed, as well as evaluation metrics for determining how well machine learning models work. The paper highlights how machine learning might improve patient outcomes and change the diagnosis of malaria, particularly in areas with limited resources. The paper concludes with a summary of key findings and contributions, limitations, and open research questions for future work.