Technical Review on Prediction and Diagnosis of Cardiovascular Diseases using Machine Learning

The most serious sickness is cardiovascular disease that people nowadays has a severe impact on their daily lives. Therefore, additional medical diagnosis and treatment are needed, as well as information transformation from the patient to the distant hospital. An accurate and prompt identification of heart disease is essential. Cardiovascular disease diagnosis by standard medical history has been deemed unreliable in several ways. Non-invasive techniques, including machine learning, are effective and trustworthy for classifying healthy and cardiac disease patients. Nevertheless, there are numerous machine learning approaches for CVD prediction. However, the data set used determines which method is the most accurate and effective. First, data pre-processing techniques are used to prepare the data. Then, we may predict CVDs at an early stage by applying machine learning methods. LR, SVM, Random Forest, Gradient Booster, and Decision Tree are the most often used algorithms. Large datasets are appropriate for Random Forest.