期刊名称：International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
印刷版ISSN：2278-1323
出版年度：2016
卷号：5
期号：9
页码：2366-2370
出版社：Shri Pannalal Research Institute of Technolgy
摘要：Cardiovascular disease is the acute disorder in theworld today. Disease control and early diagnosis of disordercan prevent from death and other diseases. Several techniqueshave been developed for assessment of cardiac risk usingstructured and unstructured patient data. Coronary ArteryDisease(CAD) is predominated disorder occurs due to severalparameters such as cholesterol level, Blood pressure, sugarlevels, smoking status, age and family history. Usually data isvery crucial for prediction of the risk and data is available inmany formats such as structured, semi structured andunstructured data, among the data formats unstructured datais vital and risk factor parameters are embedded in it. Thiswork presents an automatic method, which extracts clinical,physical and other parameters from unstructured data andthese are used for predicting the cardiac disease risk andanalyzed risk prediction methods such as Framingham,Reynolds and Prospective Cardiovascular Munster(PROCAM) using spark with python ((PySpark). Studyobserves Reynolds risk prediction method shows highsensitivity and specificity than other methods.So Reynolds risk prediction method provides better screeningtool for both men and women to know the cardiac diseases andhelps the patients that CAD can be prevented and controlled.It also provides statistical data of these methods to researchersand organizations.
