Malaria Disease Prediction using Machine Learning

The application of Machine learning will keep on resulting, particularly in the field of computerized diagnostics and estimating, because malaria is a significant general medical issue around the world, and infectious prevention requires quick and exact determination. Precisely It's still difficult to tell the difference between malaria and other diseases. Here the analysis of bloodstream indices can be used to help identify potential malaria cases for further investigation. As a novel paradigm for precision medicine, we intended to categorize machine learning (ML) algorithms capable of accurately predicting nMI, UM, and severe malaria (SM) in the bloodstream variables. The results demonstrate that Random Forest is promising and that it provides the optimum blend of precision, recall, and F1-score correctness results on datasets where they beat the Rapid Diagnostic Test