A Novel Hybrid Kasunar Forest Diseases Prediction Model for Forecasting Seasonal Vector-Borne Diseases

Alamma B. H.^{1, 2, *}, Manjula Sanjay Koti³, C. H. Vanipriya⁴

¹Research Scholar (PT), VTU Research Centre, MCA Dept., Sir M V I T, Bengaluru, India

²Assistant Prof., Dept. of MCA, Dayananda Sagar College of Engineering, Bengaluru, Karnataka, India

³Research Supervisor, Professor & Head, Dept. of MCA, Dayananda Sagar Academy of Technology and Management, Bangalore, Karnataka, India

Research Co-Supervisor, Professor & HOD, MCA Dept., Sir M. Visvesvaraya Institute of Technology, Bengaluru, Karnataka, India

Emails : alamma-mcavtu@dayanandasagar.edu; manjula.dsce@gmail.com; vanipriya.manmohan@gmail.com

Abstracts

In India, vector-borne illnesses are becoming a bigger problem.  Because the government still faces difficulties in preventing and controlling these vector-borne illnesses, they have become a burden on society.  Every year, a sizable section of India's population contracts this illness.  Due to the difference in geographical and living standard of people, it becomes difficult to regulate these diseases at early stages in the present system. The main aim of the proposed research works was to design and developing a novel hybridized Kyasanur Forest Disease (KFD) prediction model that leverages a combination of rejuvenated machine-based learning model to enhancing seasonal forecasting & detection of vector-borne diseases. By integrating advanced algorithms such as SVM, NB, LR & Multi-layer perceptron, the research seeks to improving of the accuracy & reliabilities of the prediction related to KFD cases. This hybridized approach aims to better capture the complex relationships between seasonal factors, disease symptoms, and environmental conditions, thereby providing a more effective tool for early detection and management of KFD.  

Keywords: Kyasanru Forests Disease; Support Vectored Machines; Multi-layer Perceptrons; Naïve Baye; Simulation; Result