Bioinformatics Based Intelligent Framework for Real Time Malaria Severity Classification Using Ensemble Learning Technique

This study focuses on the development of a machine learning–based approach for classifying the severity of malaria, with particular attention to cerebral malaria. The dataset was compiled from healthcare records and included both clinical symptoms such as seizures, altered mental state, headache, vomiting, and focal neurological deficits and indicators reflecting household burden, including financial strain and caregiver stress. The data were carefully prepared and divided into training and testing sets to ensure a reliable evaluation of the model’s performance. A Random Forest classifier was employed to distinguish between cerebral and non-cerebral malaria cases. By leveraging multiple decision trees, the model was trained to recognize patterns within the data and accurately predict the severity of the condition based on the observed features. The training process involved optimizing the model to improve its predictive capability across different symptom combinations and contextual factors. The results demonstrated strong performance, with the model achieving an accuracy of 0.97. Additional evaluation metrics further supported its effectiveness, with macro-average precision, recall, and F1 scores of 0.94, 0.91, and 0.92 respectively. Analysis of the dataset showed that 87.2% of cases were classified as non-cerebral malaria, while 12.8% were identified as cerebral malaria, reflecting known patterns in malaria-endemic regions. Importantly, symptoms such as seizures, altered mental status, and focal neurological deficits were found to be key indicators of severe malaria. Beyond clinical implications, the study also highlights the broader impact of the disease, particularly the financial and emotional burden placed on affected households. Overall, the findings suggest that machine learning techniques, especially Random Forest models, can serve as valuable tools in supporting more accurate diagnosis and improved management of malaria.