Impact of feature selection techniques on machine learning and deep learning techniques for cardiovascular disease prediction-an analysis

Cardio vascular disease is one of the life-threatening diseases which affects individuals worldwide. Early diagnosis may allow for the prevention or mitigation of cardiovascular diseases, which may minor mortality rates. A feasible Deep Learning and Machine Learning algorithms are used to find risk variables. Machine Learning and Deep Learning system anticipates heart diseases early on and reduce death rates from clinical data. To detect heart diseases or determine the patient's severity level, numerous research studies recently used various machine learning techniques. The volume of internationally recognised medical data sets is growing in terms of both qualities and records. This paper delivers brief outline of various feature extraction methods such as LASSO, RELIEF, RFE, MR-MR and RELIEF on deep learning and machine learning techniques for diagnosing cardiac disease. The performance metrics taken into consideration are Accuracy, Precision, Recall, F1score and the error measures are least Mean Squared Error and Mean Absolute Error. The feature selection methods with more features selected outpaced other approaches. Finally, crucial findings from the evaluated studies are outlined.