Malaria disease and grading system dataset from public hospitals reflecting complicated and uncomplicated conditions

Malaria is the leading cause of death in the African region. Data mining
can help extract valuable knowledge from available data in the healthcare
sector. This makes it possible to train models to predict patient health
faster than in clinical trials. Implementations of various machine
learning algorithms such as K-Nearest Neighbors, Bayes Theorem, Logistic
Regression, Support Vector Machines, and Multinomial Naïve Bayes (MNB),
etc., has been applied to malaria datasets in public hospitals, but there
are still limitations in modeling using the Naive Bayes multinomial
algorithm. This study applies the MNB model to explore the relationship
between 15 relevant attributes of public hospitals data. The goal is to
examine how the dependency between attributes affects the performance of
the classifier. MNB creates transparent and reliable graphical
representation between attributes with the ability to predict new
situations. The model (MNB) has 97% accuracy. It is concluded that this
model outperforms the GNB classifier which has 100% accuracy and the RF
which also has 100% accuracy.