A Novel Method for Diagnosing Heart Diseases Based on Internet of Things

Heart disease is a leading cause of mortality worldwide, necessitating effective systems for timely diagnosis and management. This study proposes an IoT-based method utilizing the Arduino platform and fuzzy logic to monitor heart health and detect individuals at risk. The system integrates sensors to measure key parameters like heart rate, blood pressure, and pulse, which are processed through a fuzzy logic algorithm. By categorizing heart rates into slow, fast, or normal states, the system enables accurate diagnosis of normal and pathological conditions. Data is efficiently processed and transmitted via IoT infrastructure to relevant medical centers, allowing healthcare professionals to act promptly, especially during critical "golden time" windows. The proposed approach emphasizes reducing data processing time and energy consumption while maintaining high accuracy. Using a dataset with 300 records from the Hungarian Heart Disease database, the model achieved a notable classification accuracy of 93.62%. The architecture includes three phases: data collection from sensors, decision-making via the fuzzy logic controller on the Arduino platform, and real-time communication of results to medical teams. This system demonstrates its potential to enhance patient outcomes through precise and swift detection of heart abnormalities, making it a valuable tool for intelligent healthcare systems in IoT-enabled smart cities.