Abstract:
Malaria is a very dangerous illness brought on by unicellular protozoan parasites of the
species Plasmodium. This disease is endemic in many parts of the world. Confirming the
presence of parasites early on in all cases of malaria permits the delivery of species-specific
antimalarial medication, which reduces the death rate and points to other illnesses in
situations where the diagnosis is negative. Nevertheless, light microscopy of thin and thick
peripheral blood (PB) films stained with May-Grünwald–Giemsa (MGG) is still the gold
standard. Since this is a labor-intensive process that relies on a pathologist's expertise,
medical professionals in regions of the world where malaria is not widespread may have
difficulties diagnosing cases of the disease. To predict malaria fever, this research used a total
of thirteen different machine-learning models. These models included the Gaussian NB,
Logistic Regression, XGB, Bagging Classifier, Random Forest Classifier, Extra Trees
Classifier, Gradient Boosting Classifier, Hist Gradient Boosting Classifier, LGBM Classifier,
Decision Tree Classifier, Ada Boost Classifier, SGD Classifier, and K neighbors Classifier.
To conduct this study, we classified cases of malaria using data obtained from chest X-ray
images. A dataset was created using 1079 patient records, and 23 attributes were used. These
attributes were gathered from the Kaggle repository. Out of these 23 attributes, 80% of the
data were used to train the model, and the remaining 20% were used to assess the validation
accuracy. It has been shown that the Gaussian NB models were the most accurate, with a
97.66% accuracy rate.
