A systematic review of Machine learning techniques for Heart disease prediction

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Shivganga Udhan
Bankat Patil

Abstract

One of the most common disease today is Heart Disease,and early diagnosis of such disease is very challenging. Machine learning includes artificial intelligence, which is implemented to solve a number of data science problems. The prediction of outcomes based on existing data is a common machine learning application.Different data mining strategies for the prediction of heart disease have been proposed with varying degrees of effectiveness and accuracy. In this paper, author provide an in-depth literature survey on systems for predicting risk of heart disease.

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How to Cite
Shivganga Udhan, & Bankat Patil. (2021). A systematic review of Machine learning techniques for Heart disease prediction. International Journal of Next-Generation Computing, 12(2), 229–239. https://doi.org/10.47164/ijngc.v12i2.208

References

  1. Abdullah A.S & R. R. Rajalaxmi, “A data mining model for predicting the coronary heart disease using random forest classifier,” in Proc. Int. Conf. Recent Trends Comput. Methods, Commun. Controls, Apr. 2012, pp. 22-25.
  2. Ansari A.Q.,” Automated Diagnosis of Coronary Heart Disease Using Neuro-Fuzzy Integrated System”, 2011 World Congress on Information & Communication Technologies 978-1-4673- 0125- [email protected] 2011 IEEE (pp 1383-1388).
  3. Ansari JyotiSoni Ujma, Sharma Dipesh. Predictive data mining for medical diagnosis: an overview of heart disease predictionII. Int. J. Comput. Appl. March 2011;17(8). (0975 – 8887).
  4. Apte & S.M. Weiss, Data Mining with Decision Trees & Decision Rules, T.J. Watson Research Center, http://www.research.ibm.com/dar/papers/pdf/fgcsaptewe issue with cover.pdf, (1997).
  5. Bhatla Nidhi, Kiran Jyoti, “A Novel Approach for Heart Disease Diagnosis using Data Mining & Fuzzy Logic”, International Journal of Computer Applications, Volume 54– No.17, (pp 16-21), September 2012, ISSN 0975 – 8887.
  6. Boshra Bahrami, Mirsaeid Hosseini Shirvani,“Prediction & Diagnosis of Heart Disease by Data Mining Techniques”, Journal of Multidisciplinary Engineering Science & Technology (JMEST) ISSN: 3159-0040 Vol. 2 Issue 2, February – 2015.
  7. Can Xiao ,Yi Li, Yimin Jiang (2020), Heart coronary artery segmentation & disease risk warning based on a deep learning algorithm DOI10.1109/ACCESS.2020.3010800, ISBN: 2169- 3536, IEEE.
  8. Carlos Ordonez Teradata, “Association Rule Discovery with the Train & Test Approach for Heart Disease Prediction”, Published in Transactions on Information Technology in Biomedicine (TITB Journal). 10(2):334-343, 2006.
  9. Chauhan Shraddha, Aeri Bani T. The rising incidence of cardiovascular diseases in India: assess- ing its economic impact. J. Prev. Cardiol. 2015;4(4):735–40.
  10. Dangare Chaitrali S., Sulabha S. Apte , “Improved Study of Heart Disease Prediction System using Data Mining Classification Techniques”, International Journal of Computer Applica- tions (IJCA) (0975 – 8887), Vol. 47, No. 10, June 2012 (pp 44-48).
  11. Das Resul, Ibrahim Turkoglu , Abdulkadir Sengur,“Effective diagnosis of heart disease through neural networks ensembles.” Expert Systems with Applications 36 (2009) 7675–7680.
  12. Domse kanchan B & Mahale kishor M. et al “Study of Machine Learning Algorithms for Special Disease Prediction using principal of component analysis” 2016 international Conference on Global Trends in Signal processing, information computing & communication.
  13. D’Souza Andrea,“Heart Disease Prediction Using Data Mining Techniques Andrea “,International Journal of Research in Engineering & Science (IJRES) ,ISSN (Online): 2320-9364, Volume 3 Issue 3 , March. 2015 , PP.74-77.
  14. E. Avci, “A new intelligent diagnosis system for the heart valve diseases by using genetic-SVM classifier”, Expert Systems with Applications, Elsevier, vol. 36, (2009), pp. 10618-10626.
  15. Fida Benish, Nazir Muhammad, Naveed Nawazish, Akram Sheeraz. Heart disease classification ensemble optimization using genetic algorithm. IEEE; 2011. p. 19–25.
  16. Gavhane A., G. Kokkula, I. Pandya, & K. Devadkar, “Prediction of heart disease using machine learning,” in Proc. 2nd Int. Conf. Electron.,Commun. Aerosp. Technol. (ICECA), Mar. 2018, pp. 1275-1278.
  17. Gupta Aanshi, ShubhamYadav, ShaikShahid, Venkanna U,(2019), HeartCare: IoT based heart disease prediction system DOI: 10.1109/ICIT48102.2019.00022, ISBN: 978-1-7281-6052- 8, IEEE.
  18. Halima EL HAMDAOUI, Sa¨?d BOUJRAF , Nour El Houda CHAOUI ,Mustapha MAAROUFI A Clinical support system for Prediction of Heart Disease using Machine Learning 10.1109/ATSIP49331.2020.9231760, ISBN: 978-1-7281-7513-3 ,IEEE.
  19. JafarJalali Seyed Mohammad, Mina Karimi , Abbas Khosravi ,Nahavandi(2019), An efficient Neuroevolution Approach for Heart Disease Detection DOI10.1109/SMC.2019.8913997, ISBN: 978-1-7281-4569-3, IEEE.
  20. KaanUyar Ahmet Ilhan. Diagnosis of heart disease using genetic algorithm based trained re- current fuzzy neural networks. 9th international conference on theory & application of soft computing, computing with words& perception. Budapest, Hungary: ICSCCW; 2017. 24-25 Aug 2017.
  21. Kaura Shivendra, AssemChandel ,Nitin Kumar Pal Heart disease-Sinus arrhythmia prediction system by neural network using ECG analysis DOI: 10.1109/PEEIC47157.2019.8976829 , ISBN: 978-1-7281-1793-5, IEEE.
  22. K.Prasanna Lakshmi, Dr.C.R.K.Reddy Fast Rule-Based Heart Disease Prediction using Associa- tive Classification Mining DOI: 10.1109/IC4.2015.7375725, ISBN: 978-1-4799-8164-9, IEEE.
  23. K. Sudhakar Study of heart disease prediction using data mining. 2014;4(1):1157–60.
  24. K Vanisree, Jyothi Singaraju. Decision support system for congenital heart disease diagnosis based on signs & symptoms using neural networks. Int J Comput Appl April 2011;19(6). (0975 8887).
  25. Liu Xiao, Wang Xiaoli, Su Qiang, Zhang Mo, Zhu Yanhong, Wang Qiugen, Wang Qian. A hybrid classification system for heart disease diagnosis based on the RFRS method. Comput. Math. Methods Med. 2017;2017:1–11.
  26. Mackay J, Mensah G. Atlas of heart disease & stroke. Nonserial Publication; 2004.
  27. Mahmood Ali Mirza , MrithyumjayaRaoKuppa Early Detection Of Clinical Parameters In Heart Disease By Improved Decision Tree Algorithm 10.1109/VCON.2010.12, ISBN: 978-1-4244- 9628-0, IEEE.
  28. M. ANBARASI, E. ANUPRIYA, N.CH.S.N.IYENGAR,“Enhanced Prediction of Heart Disease with Feature Subset Selection using Genetic Algorithm”, International Journal of Engineer- ing Science & Technology ,Vol. 2(10), 2010, 5370-5376.
  29. Masethe Hlaudi Daniel, Mosima Anna Masethe,“Prediction of Heart Disease using Classification Algorithms.” WCECS 2014, 22-24 October, 2014, San Francisco, USA. bibitem[label 1] Mackay M. Durairaj & V. Revathi, Prediction of heart disease using back propagationML- Palgorithm,” Int. J. Sci. Technol. Res., vol. 4, no. 8, pp. 235-239, 2015.
  30. M. Ganesan ,Dr.N.Sivakumar (2019), IoT based heart disease prediction & diagnosis model for healthcare using machine learning models DOI: 10.1109/ICSCAN.2019.8878850, ISBN: 978- 1-7281-1525-2, IEEE.
  31. Moloud Abdar, Sharareh R. Niakan Kalhori, Tole Sutikno, Imam Much Ibnu Subroto, Goli Arji, “Comparing Performance of Data Mining Algorithms in Prediction Heart Diseases.” Vol. 5, No. 6, December 2015.
  32. M. Silver, T. Sakara, H. C. Su, C. Herman, S. B. Dolins & M. J. O’shea, “Case study: how to apply data mining techniques in a healthcare data warehouse”, Healthc. Inf. Manage, vol. 15, no. 2, (2001), pp. 155-164.
  33. Patel Shamsher Bahadur , Pramod Kumar Yadav , Dr. D. P.Shukla, “Predict the Diagnosis of
  34. Heart Disease Patients Using Classification Mining Techniques”, IOSR Journal of Agricul- ture & Veterinary Science (IOSR-JAVS) e-ISSN: 2319- 2380, p-ISSN: 2319-2372.Volume 4, Issue 2 (Jul. - Aug. 2013), PP 61-64.
  35. Patil SB, Kumaraswamy YS. Extraction of significant patterns from heart disease warehouses for heart attack prediction. Int. J. Comput. Sci. Netw. Secur(IJCSNS) 2009;9(2):228–35.
  36. P Mamatha Alex & Shaicy P Shaji Prediction & Diagnosis of Heart Disease Patients using Data Mining Technique DOI: 10.1109/ICCSP.2019.8697977, ISBN: 978-1-5386-7595-3, IEEE.
  37. P Melillo, Izzo R, Orrico A, Scala P, Attanasio M, Automatic Prediction of Cardiovascular and Cerebrovascular Events Using Heart Rate Variability Analysis et al. (2015) Automatic Pre- diction of Cardiovascular and Cerebrovascular Events Using Heart Rate Variability Analy- sis. PLOS ONE 10(3): e0118504.
  38. R. kavitha & E kannan etal. “An efficient Framework for Heart Disease Classification using Feature Extraction & feature selection technique in Data mining” 2016
  39. Shan Xu ,Tiangang Zhu, Zhen Zang, Daoxian Wang, Junfeng Hu “Cardiovascular Risk prediction method based on CFS subset evaluation & random forest classification framework” 2017 IEEE 2ND International Conference on Big Data Analysis.
  40. Singh Manpreet, Levi Monteiro Martins, Patrick Joanis & Vijay “ Building a cardiovascular disease predictive model using structural equation model & fuzzy cognitive map”, 978-1- 5090- 0626-7/16/ 31.00 c 2016 IEEE.
  41. Tu¨lay Karayilan ,O¨ zkanKili¸c (2017), Prediction of Heart Disease Using Neural Network DOI:1109/UBMK.2017.8093512, ISBN: 978-1-5386-0930-9, IEEE.
  42. Vasighi Mahdi, Ali Zahraei, Bagheri Saeed, Vafaeimanesh Jamshid. Diagnosis of coronary heart disease based on Hnmr spectra of human blood plasma using genetic algorithm-based fea- ture selection. Wiley Online Library; 2013. p. 318-22.
  43. Xing Yanwei, Wang Jie, Yonghong Gao Zhihong Zhao. Combination data mining methods with new medical data to predicting outcome of Coronary Heart Disease. Convergence Informa- tion Technology. 2007. p. 868–72.
  44. Zeinulla Elzhan , Karina Bekbayeva , Adnan Yazici (201 Effective diagnosis of heart disease imposed by incomplete data based on fuzzy random forest DOI: 10.1109/FUZZ48607.2020.9177531, ISBN:978-1-7281-6932-3, IEEE.
  45. Ziasabounchi Negar, Iman Askerzade,“ ANFIS Based Classification Model for Heart Disease Prediction”, International Journal of Engineering & Computer Science IJECS-IJENS Vol:14 No:02, April 2014.
  46. ZubairHasan K. M. , ShourobDatta , MdZahidHasan , NusratZahan(2019),Automated Prediction of Heart Disease Patients using Sparse Discriminant Analysis 10.1109/ECACE.2019.8679279, ISBN: 978-1-5386-9111-3, IEEE. https://www.kaggle.com/abdelsamad/heart-disease-prediction- with-neural-networks