Heart Disease Prediction and Diagnosis Using IoT, ML, and Cloud Computing

Heart disease is currently regarded as the main cause of illness. Regardless of age group, heart disease is a serious condition nowadays because most individuals are not aware of their kind and level of heart disease. In this fast-paced world, it is essential to be aware of the different types of cardiac problems and the routine disease monitoring process. As per the statistics from the World Health Organization, 17.5 million deaths are because of cardiovascular disease. Manual feature engineering, on the other hand, is difficult and generally requires the ability to choose the suitable technique. To resolve these issues, IoT, machine learning models and cloud techniques, are playing a significant role in the automatic disease prediction in medical field. SVM, Naive Bayes, Decision Tree, K-Nearest Neighbor, and Artificial Neural Network are some of the machine learning techniques used in the prediction of heart diseases. In this paper, we have described various research works, related heart disease dataset, and comparison and discussion of different machine learning models for prediction of heart disease and also described the research challenges, future scope and discussed the conclusion. The main goal of the paper is to review the latest and most relevant papers to identify the benefits, drawbacks, and research gaps in this field.