부산대학교 도서관

The world has witnessed an exploding spread of cardiovascular diseases (CVD) and it has been contemplated as one of the major causes of death. In developing countries also, various physiological and psychological factors have triggered CVD to disperse at an alarming rate because of which the younger population becomes susceptible to CVD. Further, the lack of awareness about various influential factors of CVD limits its early diagnosis and treatment. Therefore, the American Heart Association provides and recommends the guidelines for effective and accurate prediction of CVD based on hypertension, cholesterol, diabetes, age, and smoking. Furthermore, the machine learning (ML) models have proved their effectiveness in identifying the hidden patterns of data and therefore, many reported literature works have employed ML techniques for the prediction of CVD. However, in the bunch of various available literature, there is a dire need for a crisp and clear review that may prove to be very effective to understand the challenges associated with CVD and its prediction along with the recent developments in the field, especially, for the young researchers. Therefore, the present proposal comprehensively summarizes the most recent developments in CVD predictions and their results have been compared based on their forecasting efficiency. [ABSTRACT FROM AUTHOR]