Hybrid Machine Learning Algorithm for Prediction of Malaria

As malaria is a fatal disease that can occur anywhere, prompt diagnosis is crucial to stopping the disease in its tracks and reducing its overall impact. To better foresee future malaria epidemics, a hybrid machine learning model was created in this research. The model performance can be improved using various strategies, such as ensemble methods or fine-tuning the hyperparameters. Choosing a proper ensemble technique impacts the model accuracy. The performance of the ensemble model was measured using several well-known machines learning algorithms, such as Decision Tree, Support Vector Machine, Naïve Bayes, K-Nearest Neighbors, and Random Forest. This methodology’s stacking strategy of ensemble technique allowed the integration of five separate algorithms. Compared to other machine learning classifiers, the results obtained using this ensemble method is superior with accuracy 92.8%, AUC 91%, recall 100%, precision 85%, F1-score 91%, specificity 100%, macro-average 94%, weighted average 98%, and error rate 5.4%. The relative importance of each variable and the degree of connection between them in explaining malaria prevalence are calculated. The results indicate that the hybrid approach is valuable for anticipating malaria outbreaks.