Abstract
Identifying cardiovascular diseases (CVD) in people at risk is a keystone for preventive cardiology. The risk forecasting tools recently suggested by medicinal plans naturally depend on the restricted numeral of predictions with sub-optimal interpretation beyond all groups of patients. Information-driven approaches depend on Machine Learning to enhance the interpretation of prediction by determining new techniques. This research helps recognize the current procedures included in predicting heart disease by classification in data mining. A review of related DM procedures that are included in heart disease prediction gives an acceptable prediction model. The main inspiration of the paper is to progress an efficient, intelligent medicinal decision system depending upon data mining techniques.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Palmer, A.J.: Computer modeling of diabetes and its complications: A report on the fifth Mount Hood challenge meeting. Value Health 16, 670–685 (2013)
Thomas, M.R., Lip, G.Y.: Novel risk markers and risk assessments for cardiovascular disease. Circ. Res. 120(1), 133–149 (2017)
Ridker, P.M., Danielson, E., Fonseca, F., Genest, J., Gotto, A.M. Jr., Kastelein, J., et al.: Rosuvastatin to prevent vascular events in men and women with elevated C-reactive protein. New England J. Med. 359(21), 2195 (2008). pmid:18997196
Kremers, H.M., Crowson, C.S., Therneau, T.M., Roger, V.L., Gabriel, S.E.: High ten-year risk of cardiovascular disease in newly diagnosed rheumatoid arthritis patients: a population-based cohort study. Arthritis Rheumatol. 58(8), 2268–2274 (2008)
D’Agostino, R.B., Vasan, R.S., Pencina, M.J., Wolf, P.A., Cobain, M., Massaro, J.M., et al.: General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation 117(6), :743–753 (2008). pmid:18212285
Conroy, R., Pyörälä, K., Fitzgerald, A.E, Sans, S., Menotti, A., De Backer, G., et al.: Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project. Eur. Heart J. 24(11), 987–1003 (2003). pmid:12788299
Sjöström, L., Lindroos, A.K., Peltonen, M., Torgerson, J., Bouchard, C., Carlsson, B., et al.: Lifestyle, diabetes, and cardiovascular risk factors ten years after bariatric surgery. New England J. Med. 351(26), 2683–2693 (2004). pmid:15616203
Siontis, G.C., Tzoulaki, I., Siontis, K.C., Ioannidis, J.P.: Comparisons of established risk prediction models for cardiovascular disease: a systematic review. BMJ. 344, e3318 (2012). pmid:22628003
Coleman, R.L., Stevens, R.J., Retnakaran, R., Holman, R.R.: Framingham, SCORE, and DECODE risk equations do not provide reliable cardiovascular risk estimates in type 2 diabetes. 30(5), 1292–1293 (2007). pmid:17290036
McEwan, P., Williams, J., Griffiths, J., Bagust, A., Peters, J., Hopkinson, P., et al.: Evaluating the performance of the Framingham risk equations in a population with diabetes. 21(4), 318–323 (2004)
MartÃn-Timón, I., Sevillano-Collantes, C., Segura-Galindo, A., del Cañizo-Gómez, F.J.: Type 2 diabetes and cardiovascular diseasehave all risk factors the same strength
Buse, J.B., Ginsberg, H.N., Bakris, G.L., Clark, N.G, Costa, F., Eckel, R., et al.: Primary prevention of cardiovascular diseases in people with diabetes mellitus. 115(1), 114–126 (2007)
Ambale-Venkatesh, B., Wu, C.O., Liu, K., Hundley, W., McClelland, R.L., Gomes, A.S., et al.: Cardiovascular event prediction by machine learning, p. CIRCRESAHA–117 (2017)
Ahmad, T., Lund, L.H., Rao, P., Ghosh, R., Warier, P., Vaccaro, B., et al.: Machine learning methods improve prognostication, identify clinically distinct phenotypes, and detect heterogeneity in response to therapy in a large cohort of heart failure patients. 7(8), e008081 (2018)
Nathan, D.M., Cleary, P.A., Backlund, J.-Y.C., Genuth, S.M., Lachin, J.M., Orchard, T.J., et al.: Intensive diabetes treatment and cardiovascular disease in patients with type 1 diabetes. 353, 2643–2653 (2005)
Shreve, J., Schneider, H., Soysal, O.: A methodology for comparing classification methods by assessing model stability and validity in variable selection. 52, 247–257 (2011)
Ordonez, C.: Improving heart disease prediction using constrained association rules (2004)
Lemke, F., Mueller, J-A.: Medical data analysis using self-organizing data mining technologies. 43(10), 1399–408 (2003)
Parthiban, L., Subramanian, R.: Intelligent heart disease prediction system using CANFIS and genetic algorithm. 3(3) (2008)
Li, W., Han, J., Pei, J.: CMAR: an accurate and efficient classification based on multiple association rules (2001)
Deepika, N., Chandrashekar, K.: Association rule for classification of Heart Attack Patients. 11(2), 253–57 (2011)
Srinivas, K., Rani, K.B., Govardhan, A.: Application of data mining techniques in healthcare and prediction of heart attacks. 2(2), 250–255 (2011)
Neelamegam, S., Ramaraj, E.: Classification algorithm in Data mining: An Overview, vol. 3, issue 5, pp. 1–5 (2013).
Borah, A., Nath, B.: Identifying risk factors for adverse diseases using dynamic rare association rule mining. Expert Syst. Appl. (2018)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Shrivastava, K., Jotwani, V. (2022). A Comparative Analysis of Various Data Mining Techniques to Predict Heart Disease. In: Jeena Jacob, I., Gonzalez-Longatt, F.M., Kolandapalayam Shanmugam, S., Izonin, I. (eds) Expert Clouds and Applications. Lecture Notes in Networks and Systems, vol 209. Springer, Singapore. https://doi.org/10.1007/978-981-16-2126-0_25
Download citation
DOI: https://doi.org/10.1007/978-981-16-2126-0_25
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-2125-3
Online ISBN: 978-981-16-2126-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)