Forecasting of Heart Attack syndrome using Logistic Regression Algorithm with Blynk App Assimilation

Heart and stroke diseases continue to be a predominant cause of mortality globally, where heart attacks are a big problem to public wellness. Early predictions & timely interventions in cases of a heart attack can reduce the fatality rate and enhance patient outcomes.This research proposes an Internet of Things systemreal-time heart attack prediction system using the logistic regression algorithm, which classifies the risk of a patient by considering age, cholesterol levels, BP, and some electrocardiographic measures. The dataset is preprocessed to make it reliable for study purposes. Logistic regression was selected because of its interpretability andsuitable for health applications. In order to make the predictive model more accessible and user-friendly, the same has been integrated with the Blynk IoT mobile application, which can visualize patient health data seamlessly collected from sensors and microcontrollers. The system architecture combines IoT-based data acquisition with cloud-based analytics, enabling real-time alerts and risk assessment on the user’s smartphone. The study outcomes show that the suggested model provides a high level of precision inpredicting heart attack risk while maintaining transparency in decision-making, which is essential for clinical adoption. Integration of ML with IoT platforms underlines the potentials of intelligent healthcare systems in supporting preventive care, empowering patients, and assisting medical professionals in early diagnosis.