An Approach to Prediction of Cardiovascular Diseases using Machine and Deep Learning Models - Bhagyalaxmi Singirikonda; Muktevi Srivenkatesh (International Journal of Intelligent Systems and Applications in Engineering, 2022)
In this article, we examined approximately 550 patient records in order to determine major risk variables that may be the root cause of cardiac issues. This study attempts to offer a piece of work that may be used as an instant step toward determining a probability assessment for the heart condition. The many risk factors are those that may be the major cause of the emergence of a cardiac condition. In this work, we examined several classification methods to diagnose the heart condition. The data was gathered from five separate Indian cities and also from people of all ages. We used the bidirectional LSTM model (BDLSTM), which was trained with experimental data from the source. The primary goal of this type of activity is to present a clear solution that will allow the patient to know statistically whether a heart problem is likely to occur. This answer is not a substitute for a healthcare professional, but rather a supplement to any doctor’s diagnostic procedure. This look after transparency in the treatment of a physician and a patient. The model is evaluated based on both positives and negatives of false generated by model, and the best method for prediction is chosen. The results showed that two of these four algorithms were more accurate than 98.5% of the time.