Predicting Cardiovascular Disease: A Scoping Survey on different Datasets and DL/ML Models using ECG

Nowadays, cardiovascular disease is the leading global cause of death. Predicting heart disease has become a complex endeavor, requiring both expertise and advanced knowledge. In recent years, Internet of Things (IoT) technology has found application in healthcare systems to gather sensor data for the diagnosis and prediction of heart disease. Ongoing research explores various automated methods for identifying these diseases, aiding both patients and professionals in treatment of Healthcare. This article conducts a review focused on disease identification, classification, and recognition using sensors of Electrocardiogram (ECG) signals, examining studies published between 2020 and 2023 across diverse scientific databases. The review provides both quantitative and qualitative analyses of numerous scientific papers, revealing several machine learning and deep learning models. Notably, Convolutional Neural Network (CNN) and Support Vector Machine (SVM) emerged as the most frequently employed algorithms. The insights from this review can guide the development of systems promoting patient autonomy.