Integrated Healthcare System for Saline Monitoring, Patient Communication, and Heart Disease Prediction Using IoT and Machine Learning Algorithms

The integration of IoT and ML technologies in the healthcare sector has brought significant transformations, enhanced patient care and reducing costs. This research paper presents an innovative idea focuses on developing a comprehensive system for healthcare, incorporating saline fluid control and monitoring, patient communication, and predictive analysis for heart disease. The saline control and monitoring feature employs an ultrasonic sensor to detect fluid levels, with data processed by a NodeMCU. When fluid levels exceed a threshold, alerts are triggered through a buzzer, red LED, and WhatsApp messages sent to medical authorities and a designated relative. The integration of WhatsApp's API bot streamlines messaging. Additionally, to ensure patient safety, a servo motor clamps the saline tube if medical authorities don't respond promptly, preventing blood backflow. The system also includes a glove with sensors for communication among bedridden patients. Heartbeat and temperature sensors monitor vital signs and medical history, transmitted to a software interface developed using ML algorithms and a Java-based GUI. This facilitates data management and analysis for medical authorities, enabling the prediction of heart disease likelihood based on new patient data using ML algorithms. The research paper aims to enhance healthcare efficiency, improve patient care, and reduce response time during critical situations. The key findings demonstrate successful implementation of a comprehensive healthcare system monitoring saline fluid, providing timely alerts, enabling patient communication, and predicting heart disease likelihood. In conclusion, the system utilizes advanced technologies to enhance patient safety, facilitate communication, and assist medical authorities in making informed decisions.