In recent times the prevalence of heart attacks among the youth is adding, which lead to the reason for death everywhere. These heart-related issues are endorsed due to fast-paced lifestyles and food habits. Not only adult youth are also affecting by heart attack. Inactivity, stress, insomnia, smoking, drinking, diabetes, hypertension, and obesity add to this health risk. The main cause for the increase in the death rate ensues due to the delay in detecting the symptoms or lack of early diagnosis. This paper proposes architectural design and solution to predict heart attacks with machine learning algorithms. The symptoms of heart attack can be traced by means of integrating the IoT with machine learning algorithms and medical care systems. In addition, a real-time patient monitoring system is developed to observe heart disease in patients, which assists a person to track his/her health condition easily, economically, and effectively. The system also detects patient risk levels based on different heart-related parameters used for the prudence of abnormal heart function.

Internet of Things (IoT), heart attack prediction system, machine learning, Android smartphone