SMART COMMUNITY HEALTH MONITORING AND EARLY WARNING SYSTEM FOR WATER-BORNE DISEASES

Abstract

Water-borne diseases such as cholera, typhoid, dysentery and diarrhea are major public health concerns, particularly in developing countries where access to clean drinking water and proper sanitation is limited. Traditional water quality monitoring methods rely on manual sampling and laboratory testing, which are time-consuming and often fail to provide early warning before disease outbreaks occur. This project proposes a Smart Community Health Monitoring and Early Warning System that integrates Internet of Things (IoT) technology and machine learning techniques to continuously monitor water quality and predict potential disease risks. The system uses to IoT sensors such as PH sensors, turbidity sensor and temperature sensors to monitor important water quality parameters in real time. The collected data is transmitted to a cloud platform where it is stored and analyzed. Machine learning algorithms analyze both environmental data and community health reports to detect abnormal patterns and identify possible contamination risks.