An Initiative To Prevent Japanese Encephalitis Using Genetic Algorithm And Artificial Neural Network

Abstract

Japanese Encephalitis primarily affects children. Most adults in endemic countries have natural immunity after childhood infection, but individuals of any age may be affected. This work deals with the data of those who are affected. The primary step is studying the data obtained to Figure out the unique and similar symptoms which are present in Japanese Encephalitis in comparison with normal Viral Fever. Machine Learning algorithms are used to carry out this work. The Genetic Algorithm is used for optimization and generation of fittest string for the input data. To obtain precise results along with the justification, the Attribute Selection algorithm is also used. The main objective of the work is to create preventive awareness of the disease at the initial stage. Extract the essential features of biotest from the affected person, which is taken into consideration with the genetic algorithm and Attribute Selection algorithm. Genetic algorithms give higher quality for the optimized problem and produce an approximate result using the Attribute Selection algorithm with factor analysis. The percentage of improvement on using these algorithms is 96%. OpenCV color change detection and Artificial Neural Network (ANN) is used to detect the change in the color and infection information of the Brain cell. The results outperform with the existing methodologies to detect whether the cell is parasitized or uninfected. The percentage of omprovement on using this algorithm is 99%.