Machine Learning and Data Mining Approaches for Infectious Disease Surveillance and Outbreak Management in Healthcare

Infectious diseases pose significant threats to public health, making their surveillance and outbreak management paramount. Leveraging the power of machine learning and data mining, our proposed methodology, “Machine Learning and Data Mining Approaches for Infectious Disease Surveillance and Outbreak Management in Healthcare,” offers a comprehensive solution for modern healthcare practices. This methodology integrates advanced algorithms, including Support Vector Machines (SVM), Random Forest, and K-Means clustering, to enhance disease surveillance and management. The results of our comparative analysis demonstrate the superiority of our proposed method over traditional approaches. We compared our method with original methods such as Support Vector Machines, Random Forest, Neural Networks, K-Nearest Neighbors, Decision Trees, Time Series Analysis, Clustering (K-Means), Naive Bayes, Principal Component Analysis, and Association Rule Mining. Our method consistently outperformed the original methods in various performance metrics, including accuracy, precision, recall, F1 score, AUC-ROC, and computational efficiency. These results affirm the potential of our proposed methodology to revolutionize infectious disease management, offering data-driven, scalable, and adaptive solutions to healthcare practitioners.