A proposed wearable smart system using hybrid computing and machine learning algorithms (SVM, AdaBoost, RF) was developed to monitor heart health remotely and detect preliminary heart attacks.
A proposed machine learning-based wearable system utilizing hybrid computing architecture demonstrates potential for the remote, early detection and monitoring of heart attacks.
Every year, the percentage of people affected by cardiovascular diseases increases drastically. Out of them, a heart attack is the most prominent and painful disease. According to the World Health Organization, approximately 17.5 million people lose their lives yearly due to this disease, which is alarming. The remarkable advancement in wearable technology has opened doors to propose many effective smart solutions to tackle this disease efficiently. Furthermore, early diagnosis of heart attack proliferates the compatibility of meditation and expedites the diagnostic recommendation by clinical experts. Considering this problem’s sensitivity, we proposed a wearable smart and early Heart Attack diagnosis system and adopted a decentralized computational phenomenon using hybrid computing architecture. It reflects better response time and minimal latency to detect a heart attack in its preliminary stage for homage patients. The proposed system can monitor and trigger the patient current heart status classified on required heart diagnosis parametric sensors assembled on the patient’s body with the help of an android application. In this study, three models are developed using the Support Vector Machine (SVM), Adaptive Boosting (AdaBoost), and Random Forest (RF) algorithms for the classification. Performance measures: accuracy, error rate, and response time are used to evaluate the proposed system. Our research findings promise that it can be implemented on patients diagnosed with the risk of a heart attack to monitor their heart health remotely and prevent sudden heart failure without impeding a person’s everyday life.
Hannan et al. (Sat,) conducted a other in Heart attack / cardiac arrest. Wearable smart heart attack diagnosis system using hybrid computing was evaluated on Accuracy, error rate, and response time. A proposed wearable smart system using hybrid computing and machine learning algorithms (SVM, AdaBoost, RF) was developed to monitor heart health remotely and detect preliminary heart attacks.