Browsing by Author "Dennis Arthur Nyanzi"

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Predictive analysis of pediatric in hospital malaria mortality using machine learning for early clinical intervention
(Uganda Christian University, 2026-06-17) Dennis Arthur Nyanzi
Malaria remains a major public health challenge in Uganda especially among the children. Pediatric patients are more vulnerable because of weaker immune systems and faster dis-ease progression. Delayed assessment increases mortality rates. This study aimed at using machine learning to predict mortality among children admitted with malaria. An experi-mental computational research design was adopted on retrospective secondary data. Five supervised machine learning models were developed: Decision Tree Classifier, Random Forest Classifier, Naive Bayes, Logistic Regression and Gradient Boosting model. These models were developed to predict mortality among pediatric malaria patients. Models were evaluated using precision, recall, F1 score, Confusion Matrix, Learning curves and Re-ceiver Operating Characteristic Area Under Curve. K fold cross validation was applied during training and Grid search was used for hyperparameter tuning. The results showed that the Naive Bayes classifier performed best followed by the Logistic Regression model having achieved the highest receiver operating characteristic area under curve of 0.899 and 0.885 respectively. The models were able to accurately predict the likelihood of mortality among children admitted with malaria through a combination of both clinical and labora-tory factors. Machine learning demonstrated potential in early identification of high risk pediatric malaria patients for timely clinical intervention.