Abstract
Artificial Intelligence (AI) has laid down the platform where all the emerging fields can benefit for detection of patterns. In similar, evolving AI technology has revolutionized the traditional healthcare technology to other level of technological advancement. Hence, Machine learning paradigms are designed to transform the era of learning and retrieval of hidden patterns from the healthcare databases. The current scope of the work focuses on detecting patterns from car diac care database using machine learning. The database comprise of several features such as age, gender, smoking variable, blood pressure, diabetes, alcohol consumption, sleep variables and other features which can be the potential cause for the prognosis of the disease Further, the databases applicability is measured with different classifiers such K nearest neighbours (KNN), Adaboost, XGboost, Gradient Boost, Decision Tree Logistic Regression, and Random Forest Classifiers to determine the most relevant classifier for the prognosis of the disease. The results suggest that Xgboost works efficiently with higher accuracy rate as compared to other classifiers.
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
Chen, M., Hao, Y., Zhang, N.: Hospital admission prediction based on healthcare information system data. J. Biomed. Inform. 45(5), 905–911 (2012)
Khera, A.V., et al.: Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat. Genet. 50(9), 1219–1224 (2018)
Musunuru, K., et al.: Basic concepts and potential applications of genetics and genomics for cardiovascular and stroke clinicians: a scientific statement from the American Heart Association. Circ. Genom. Precis. Med. 12(11), e000046 (2019)
Bibault, J.E., Giraud, P., Burgun, A., Big Data and Machine Learning in Radiation Oncology Collaboration (B-DaMIC): Big data and machine learning in radiation oncology: state of the art and future prospects. Cancer Lett. 471, 1–8 (2019)
Zhang, Z., Chen, J., Ma, G., Yang, Y., Wu, Y.: Predicting the onset of acute myocardial infarction with a machine learning model using population-based medical databases. Front. Physiol. 10, 130 (2019)
Obermeyer, Z., Powers, B., Vogeli, C., Mullainathan, S.: Dissecting racial bias in an algorithm used to manage the health of populations. Science 366(6464), 447–453 (2019)
Fonseca, C.G., et al.: The cardiac atlas project—an imaging database for computational modeling and statistical atlases of the heart. Bioinformatics 36(16), 4160–4167 (2020)
Maurovich-Horvat, P., Ferencik, M., Voros, S., Merkely, B., Hoffmann, U.: Comprehensive plaque assessment by coronary CT angiography. Nat. Rev. Cardiol. 16(12), 723–737 (2019)
Hazra, A., Mandal, S., Gupta, A., Mukherjee, A.: Heart disease diagnosis and prediction using machine learning and data mining techniques: a review. Adv. Comput. Sci. Technol. 10, 2137–2159 (2017)
Varma, G., Chauhan, R., Singh, D.: Sarve: synthetic data and local differential privacy for private frequency estimation. Cybersecurity 5, 26 (2022). https://doi.org/10.1186/s42400-022-00129-6
Patel, J., Upadhyay, P., Patel, D.: Heart disease prediction using machine learning and data mining technique. J. Comput. Sci. Electron. 7, 129–137 (2016)
Zinat Motlagh, S.F., Chaman, R., Ghafari, S.R., Parisay, Z., Golabi, M.R., Eslami, A.A., et al.: Knowledge, treatment, control, and risk factors for hypertension among adults in Southern Iran. Int. J. Hypertens. 2015 (2015)
Noncommunicable diseases Fact Sheet. World Health Organization (2021). https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases
Mittal, B.V., Singh, A.K.: Hypertension in the developing world: challenges and opportunities. Am. J. Kidney Dis. 55(3), 590–598 (2010)
Gaziano, T.A., Bitton, A., Anand, S., Abrahams-Gessel, S., Murphy, A.: Growing epidemic of coronary heart disease in low-and middle-income countries. Curr. Probl. Cardiol. 35(2), 72–115 (2010)
Buettner, R., Schunter, M.: Efficient machine learning based detection of heart disease. In: 2019 IEEE International Conference on E-Health Networking, Application & Services (HealthCom). IEEE (2019)
Roth, G.A., Mensah, G.A., Johnson, C.O., Addolorato, G., Ammirati, E., Baddour, L.M., et al.: Global burden of cardiovascular diseases and risk factors, 1990–2019: update from the GBD 2019 study. J. Am. Coll. Cardiol. 76(25), 2982–3021 (2020)
Hemingway, H., Langenberg, C., Damant, J., Frost, C., Pyörälä, K., Barrett-Connor, E.: Prevalence of angina in women versus men: a systematic review and meta-analysis of international variations across 31 countries. Circulation 117(12), 1526–1536 (2008)
Stanaway, J.D., Afshin, A., Gakidou, E., Lim, S.S., Abate, D., Abate, K.H., et al.: Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. The Lancet 392(10159), 1923–1994 (2018)
Cardiovascular disease mortality in the developing countries. World Health Statist Quart, vol. 46, pp. 89–150 (1993)
Yadav, D.P., Sharma, A., Singh, M., Goyal, A.: Feature extraction based machine learning for human burn diagnosis from burn images. IEEE J. Transl. Eng. Health Med. 7, 1–7 (2019). Art no. 1800507. https://doi.org/10.1109/JTEHM.2019.2923628
Kopp, W.: How western diet and lifestyle drive the pandemic of obesity and civilization diseases. Diabetes Metab. Syndr. Obesity Targets Ther. 12, 2221 (2019)
Lalkhen, H., Mash, R.: Multimorbidity in non-communicable diseases in South African primary healthcare. S. Afr. Med. J. 105(2), 134 (2015)
Hajar, R.: Risk factors for coronary artery disease: historical perspectives. Heart Views Official J. Gulf Heart Assoc. 18(3), 109–114 (2017). https://doi.org/10.4103/HEARTVIEWS.HEARTVIEWS_106_17
Kandaswamy, E., Zuo, L.: Recent advances in treatment of coronary artery disease: role of science and technology. Int. J. Mol. Sci. 19(2), 424 (2018). https://doi.org/10.3390/ijms19020424
Shahwan-Akl, L.: Cardiovascular disease risk factors among adult Australian-Lebanese in Melbourne. Int. J. Res. Nurs. 1, 1–7 (2010)
Helma, C., Gottmann, E., Kramer, S.: Knowledge discovery and data mining in toxicology. Stat. Methods Med. Res. 9, 329–358 (2000)
Kiyong, N., Heon Gyu, L., Keun Ho, R.: Data mining approach for diagnosing heart dis- ease. Korea Research Institute of Standards and Science, vol. 10, no. 2, pp. 147–154 (2007)
Kaggle. https://www.kaggle.com/code/andls555/heart-disease-prediction/notebook
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Chauhan, R., Singh, D. (2024). Maximizing Accuracy in AI-Driven Pattern Detection in Cardiac Care. In: Choi, B.J., Singh, D., Tiwary, U.S., Chung, WY. (eds) Intelligent Human Computer Interaction. IHCI 2023. Lecture Notes in Computer Science, vol 14531. Springer, Cham. https://doi.org/10.1007/978-3-031-53827-8_17
Download citation
DOI: https://doi.org/10.1007/978-3-031-53827-8_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-53826-1
Online ISBN: 978-3-031-53827-8
eBook Packages: Computer ScienceComputer Science (R0)