Abstract
Researchers have paid close attention to the field of medicine. Several factors have been blamed for human early mortality by a sizable number of researchers. The relevant research has established that diseases are brought on by a variety of factors, one of which is heart-related illnesses. Numerous scholars suggested unconventional ways to prolong human life and aid medical professionals in the diagnosis, treatment, and management of the cardiac disease. Some practical techniques help the expert make a conclusion, yet every effective plan has limitations of its own. In data mining, support vector machines (SVMs) are an important classification technique. It is a method of supervised classification. It locates a hyperplane to classify the intended classes. A variety of heart-related illnesses make up heart disease. Vascular problems such as arrhythmia, weak myocardium, congenital heart disease, cardiovascular disease, and coronary artery disease are included in this category. A common form of heart disease is coronary artery disease. It causes a heart attack by decreasing the blood supply to the heart. Support vector machines are used in this study to assess the data set from the UCI machine learning repository made up of heart disease patients. Patients with cardiac disease are accurately classified, as expected. Python is used as the programming language for implementation.
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References
AliveKor [Online]. https://www.alivecor.com/how-it-works
Ananey-Obiri D, Sarku E (2020) Predicting the presence of heart diseases using comparative data mining and machine learning algorithms. Int J Comput Appl 975
Atallah R, Al-Mousa A (2019) Heart disease detection using machine learning majority voting ensemble method. In: 2019 2nd international conference on new trends in computing sciences (ICTCS), Amman, Jordan, IEEE, pp 1–6
Basha N, Ashok Kumar PS, Gopal Krishna C, Venkatesh P (2019) Early detection of heart syndrome using machine learning technique. In: 2019 4th international conference on electrical, electronics, communication, computer technologies and optimization techniques (ICEECCOT), Mysuru, India, 11th June, pp 1–5
Boudi FB (2016) Risk factors for coronary artery disease [Online]. https://emedicine.medscape.com/article/164163-overview
Gavhane A, Pandya I, Kokkula G, Devadkar K (2018) Prediction of heart disease using machine learning
Japp AG, Gulati A, Cook SA, Cowie MR, Prasad SK (2016) The diagnosis and evaluation of dilated cardiomyopathy. J Am Coll Cardiol. American College of Cardiology Foundation Published by Elsevier
Johnson-Coyle L, Jensen L, Sobey A (2012) Peripartum cardiomyopathy: review and practice guidelines. Am J Crit Care 21(2)
Karayõlan T, Kõlõç Ö (2017) Prediction of heart disease using neural network
Kohli PS, Arora S (2018) Application of machine learning in disease prediction. In: 2018 4th international conference on computing communication and automation (ICCCA, Greater Noida, India, 29th July, pp 1–4
Lin C-H, Yang P-K, Lin Y-C, Fu P-K (2020) On machine learning models for heart disease diagnosis. In: 2nd IEEE Eurasia conference on biomedical engineering, healthcare and sustainability 2020, Tainan, Taiwan, pp 158–161
Machine learning based decision support systems (DSS) for heart disease diagnosis: a review. Online, 25 March 2017. https://doi.org/10.1007/s10462-01
Maru A, Sharma AK, Patel M (2021) Hybrid machine learning classification technique for improve accuracy of heart. In: Proceedings of the sixth international conference on inventive computation technologies [ICICT 2021] IEEE Xplore Part Number: CFP21F70-ART. ISBN: 978-1-7281-8501-9, pp 1107–1110
Motarwar P, Duraphe A, Suganya G, Premalatha M (2020) Cognitive approach for heart disease prediction using machine learning. In: 2020 international conference on emerging trends in information technology and engineering (ic-ETITE), 27 April, Vellore, India, pp 1–5
Ngom F, Fall I, Camara MS, Alassane BAH (2020) A study on predicting and diagnosing noncommunicable diseases: case of cardiovascular diseases
Patil SB, Kumaraswamy YS (2009) Extraction of significant patterns from heart disease warehouses for heart attack prediction. IJCSNS 9(2):228–235
Purusothaman G, Krishnakumari P (2015) A survey of data mining techniques on risk prediction: heart disease. Indian J Sci Technol 8(12). Alexander CA, Wang L (2017) Big data analytics in heart attack prediction. J Nurs Care 6(393):1168–2167
Sebastian VB, Unnikrishnan A, Balakrishnan K (2012) Grey level co-occurrence matrices: generalisation and some new features. Int J Comput Sci Eng Inf Technol (IJCSEIT) 2(2):151–157
Sharma H, Rizvi MA (2017) Prediction of heart disease using machine learning algorithms: a survey
Soni J et al (2011) Predictive data mining for medical diagnosis: An overview of heart disease prediction. Int J Comput Appl 17(8):43–48
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Lutimath, N.M., Mouli, C., Gowda, B.K.B., Sunitha, K. (2023). Prediction of Heart Disease Using Hybrid Machine Learning Technique. In: Rai, A., Kumar Singh, D., Sehgal, A., Cengiz, K. (eds) Paradigms of Smart and Intelligent Communication, 5G and Beyond. Transactions on Computer Systems and Networks. Springer, Singapore. https://doi.org/10.1007/978-981-99-0109-8_15
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