In many tropical and subtropical areas of the globe, malaria—a potentially fatal disease spread by mosquitoes—remains a serious public health concern. Malaria continues to be a leading source of disease and mortality despite ongoing attempts at prevention and control, especially in poor nations. Reducing the severity of the disease, avoiding complications, and lowering death rates all depend on an early and accurate diagnosis. However, laboratory-based procedures like blood smear microscopy and quick diagnostic tests, which call for qualified medical personnel, specialized equipment, and sufficient healthcare infrastructure, constitute a major component of traditional diagnostic methods. In rural, isolated, and deprived places, these requirements make timely diagnosis challenging, which increases health risks and delays treatment. The use of machine learning methods into healthcare systems has demonstrated encouraging outcomes in terms of enhancing forecasting of illnesses and diagnostic accuracy in recent years. Large datasets may consist of hidden patterns, and machine learning models can produce accurate predictions based on input features. Keywords: Malaria Prediction, Machine Learning, Random Forest Classifier, Symptom-Based Diagnosis, Healthcare Analytics, Django Web Application.
G et al. (Sun,) studied this question.