Enhancing Meningitis Type Diagnosis through a Novel Hybrid Ensemble of Bagging and Gradient Boosting
Sathiya Priya S, Muhammad Hilmi Amanullah, T. Kujani · 2024
Meningitis, an inflammation of the meninges surrounding the brain and spinal cord, remains a significant public health concern globally. Accurately diagnosing different types of meningitis is crucial for providing appropriate treatment and improving patient outcomes. In this study propose a novel hybrid ensemble approach that combines the strengths of bagging and gradient boosting algorithms to enhance the diagnosis of meningitis types. The bagging technique involves training multiple decision tree models on random subsets of the training data and aggregating their predictions to reduce over fitting and improve generalization. By integrating these two powerful ensemble methods, this approach aims to capture complex patterns in the data and provide robust and accurate predictions. The proposed hybrid ensemble was evaluated on a comprehensive dataset of meningitis patients, including clinical symptoms, laboratory tests, and demographic information. The model's performance was assessed using various metrics such as accuracy, F1-score, and area under the receiver operating characteristic (ROC) curve. Our results demonstrate that the hybrid ensemble significantly outperformed individual bagging and gradient boosting models, as well as other commonly used algorithms like logistic regression and support vector machines. The optimized hyper parameters and selected features provide insights into the most important factors influencing meningitis types, which can aid clinicians in making informed decisions. This study highlights the potential of hybrid ensemble methods in enhancing the diagnosis of complex medical conditions like meningitis. Combining bagging and gradient boosting can lead to better precision, F1 score, and recall compared to using them separately, the values are 0.94,0.95. 0.96 respectively. The proposed approach can be easily adapted to other disease classification tasks and serves as a valuable tool for improving patient care and reducing healthcare costs associated with misdiagnosis. Future research should focus on validating the model's performance on larger and more diverse datasets and exploring the integration of additional machine learning techniques to further enhance diagnostic accuracy.