P-2006. Development of a Machine Learning Model for the Accurate Diagnosis of Common Tropical Febrile Illnesses

Abstract Background Acute febrile illnesses (AFIs) such as dengue, malaria, scrub typhus, leptospirosis etc, prevalent in tropical regions, account for 17% of the global disease burden. Overlapping clinical features and limitations of current diagnostic methods—including false positives, sensitivity/specificity issues made the diagnosis complicated. With increasing digital integration in healthcare, artificial intelligence (AI) offers a promising solution for improving diagnostic accuracy and efficiency. Our study aimed to develop an AI-based tool to aid differential diagnosis of AFIs and support clinical decision-making. Fig 1: Features selected through Recursive Elimination and MulticollinearityBased on RecursiveFeatureElimination (RFE), a set of high-ranking features was initially identified. These features were then subjected to multicollinearity assessment to eliminate redundant variables with strong linear relationships. The final set of 20 non-collinear, clinically relevant features was selected for model development and is presented in Fig₁Fig 2: Accuracy of the developed models Methods A retrospective cross-sectional study analyzed records of 800 patients (200 per disease). Clinical data were extracted and preprocessed (cleaning, scaling, imputation). Feature selection was conducted using RecursiveFeatureElimination (RFE) and multicollinearity assessment. Models were developed using supervised machine learning algorithms— RandomForest (RF), NaïveBayes (NB), LogisticRegression (LR), SupportVectorMachine (SVM), K-NearestNeighbors (KNN), and DecisionTree (DT). A stacking classifier served as a meta-model. Performance was evaluated using accuracy, precision, recall, and F1-score. Fig 3: Confusion matrix for Stacking ClassifierTable 1: Other performance Metrics Results Based on RFE, set of high-ranking features were identified and were then subjected to multicollinearity assessment. The final set of 20 non-collinear, clinically relevant features was selected for model development (presented in Fig₁). Among the developed predictive models, the RF demonstrated highest classification accuracy (87%), followed by LR (86. 5%), SVM (85. 5%), KNN (73%), DT (72. 5%), and NB (70. 5%), (shown in Fig₂). Additional performance metrics, including precision, recall, and F1-score, are summarized in Table 1. A stacking classifier, integrating the predictions of all base models, achieved an overall accuracy of 89% on training dataset. The confusion matrix for the stacking model is presented in Fig₃ Conclusion By integrating clinical data with advanced feature selection techniques, AI-based tool can be the future diagnostic aiding tool for screening the tropical diseases. Disclosures All Authors: No reported disclosures