Cholera detection system using CNN machine learning algorithm

Abstract

A comprehensive cholera detection system leveraging cutting-edge technologies such as neural networks, machine learning, chatbots, live maps, and real-time statistical graphs is proposed. The system integrates a user-friendly chatbot interface to interact with individuals, prompting them to input relevant health information and symptoms. Behind the scenes, neural networks and machine learning algorithms analyze the data to detect potential cholera cases, offering users instant insights into their health status. The system incorporates live maps to track reported cases geographically, enabling a swift response from health authorities. Moreover, real-time statistical graphs provide dynamic visualizations of cholera trends, aiding in the identification of potential outbreak hotspots. By amalgamating these technologies, the cholera detection system not only facilitates early diagnosis and intervention but also enhances public health monitoring and management, contributing to the overall control and prevention of cholera outbreaks.