Detection of early spread of vector borne disease with personalized treatment strategies using machine learning
Arun B Prasad, K. Prabhu, Kavyashree Nagarajaiah, Juhie Agarwal, A. Srilakshmi, Rakesh Verma · 2024
We will employ a number of machine learning models such as support vector machines, decision trees, random forests and naive Bayes to predict early detection and personalized treatment for periodontal disease. We experiment with different datasets to train and validate models of diseases, weather conditions and patient symptoms. The performance metrics evaluated using the following scores are-ROC AUC, PR AUC and Random Forest (for high accuracy) The analysis also reveals the key variables that determine whether a disease will become more severe, which in turn informs efforts to manage and treat the disease. “Research of this kind has the potential to support public health strategies and treatment for chronic diseases around the world,” she said.