Advanced Meningitis Diagnostic Modeling: Combining Fuzzy Cognitive Maps and Decision Trees for Superior Predictive Performance

Accurate and timely diagnosis of meningitis, a critical neurological condition, is paramount for effective patient management. This paper presents an advanced diagnostic modeling approach aimed at enhancing the precision of meningitis detection by synergistically integrating Fuzzy Cognitive Maps (FCMs) and Decision Trees. By leveraging the dynamic knowledge representation of FCMs to model complex interrelationships among clinical variables, and employing Decision Trees for robust classification, this strive to achieve superior predictive performance. Essential to this methodology is the incorporation of Cerebrospinal Fluid (CSF) analysis, a pivotal component in meningitis diagnosis, alongside clinical symptoms, laboratory findings, and patient demographics within the comprehensive dataset. The Decision Tree model, optimized using Randomized Search, achieved an accuracy of 88%. The findings demonstrate that the hybrid FCM-Decision Tree model, incorporating CSF data, significantly improves diagnostic accuracy when compared to traditional machine learning models and those without CSF information. This work underscores the potential of integrating FCMs and Decision Trees, especially with crucial diagnostic data like CSF analysis, to enhance the diagnosis of complex medical conditions such as meningitis. The proposed method offers a valuable tool for improving patient care and potentially mitigating the impact of misdiagnosis on healthcare systems. Future efforts will focus on validating the model on larger, more diverse datasets, exploring the integration of other advanced machine learning techniques, and further analyzing the specific contributions of FCM modeling and CSF data to diagnostic accuracy, with the goal of increasing clinical utility.