Authors - Lakshitaa Sehgal, Sarthak Sood, Sanyam Sood, Anshal Aggarwal Abstract - Cardiovascular diseases (CVD) are widespread in the population and frequently result in fatalities. According to data from a recent poll, use of tobacco, high blood pressure, cholesterol, and obesity contribute to an increasing mortality rate. The need of the hour is to conduct research on the variances of these factors and how they affect CVD. With the use of machine learning and artificial intelligence, this comprehensive study can be completed as their extensive methodologies would help in the prediction or detection of Cardiovascular Disease and discover their patterns in the vast amount of data. The predictive results might help doctors and clinicians in early detection of CVD in patients and might save many lives. After preprocessing the dataset, the classification, machine learning, data mining, hybrid and deep learning models used to predict cardiovascular risk are compared and reported in this paper. This paper compares and summarizes the performance accuracies of several models. The hybrid model with Light Gradient Boosting Machine (LGBM) and Artificial Neural Network (ANN) has an accuracy of 89.46% and is the best model.
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