Current research is utilizing machine learning techniques to help diagnose cardiovascular disease. The machine learning techniques and “big data” play a tremendous role in the healthcare sectors and medical industries. Machine learning identifies the risk patterns in high volumes of data and provides conclusive solutions to the medical problem with the help of these existing data. While utilizing large and readily available patient's medical status datasets, a precise prediction and diagnosis of the symptoms of cardiovascular disease, together with the severity of a heart attack may be predicted automatically, as well as visualization and monitoring of the patient’s data in real time.

Papers:

Mamta Sharma, Farheen Khan, Vishnupriya Ravichandran, “Comparing Data Mining Techniques Used For Heart Disease Prediction,” f Engineering and Technology, vol.5, Issue 6, June 2017.

Vladimir S. Kublanov, Anton Yu. Dolganov, David Belo, and Hugo Gamboa, “Comparison of Machine Learning Methods for the Arterial Hypertension Diagnostics,” Applied Bionics and Biomechanics, vol. 2017, Article ID 5985479, 13 pages, 2017.

Liangqing Zhang, Cuirong Yu, Chunrong Jin, et al., “A Remote Medical Monitoring System for Heart Failure Prognosis,” Mobile Information Systems, vol. 2015, Article ID 406327, 12 pages, 2015.

Zoiner Tejada, 2017, “Mastering Azure Analytics: Architecting in the Cloud with Azure Data Lake, HDInsight, and Spark”, 1st ed.

Karthik Ramasubramanian Abhishek Singh, 2017, “Machine Learning Using R - A Comprehensive Guide to Machine Learning”.