Mathematical model development for the hybridized KFD prediction model for seasonal forecasting of vector borne diseases

Abstract

In this paper, the mathematical model development of the proposed method is presented in a nutshell.The main aim of the proposed research work is to design and develop a novel hybridized Kyasanur Forest Disease(KFD) prediction model that leverages a combination of rejuvenated machine learning models to enhance seasonalforecasting and detection of vector-borne diseases. By integrating advanced algorithms such as Support VectorMachines, Naive Bayes, Logistic Regression, and Multi-layer Perceptrons, the research seeks to improve theand reliability of predictions related to KFD cases. This hybridized approach aims to better capture thecomplex relationships between seasonal factors, disease symptoms, and environmental conditions, thereby
providing a more effective tool for early detection and management of KFD.