Hybrid genetic algorithm and a fuzzy logic classifier for heart disease diagnosis

  • G. Thippa Reddy
  • M. Praveen Kumar Reddy
  • Kuruva Lakshmanna
  • Dharmendra Singh Rajput
  • Rajesh Kaluri
  • Gautam SrivastavaEmail author
Special Issue


For the past two decades, most of the people from developing countries are suffering from heart disease. Diagnosing these diseases at earlier stages helps patients reduce the risk of death and also in reducing the cost of treatment. The objective of adaptive genetic algorithm with fuzzy logic (AGAFL) model is to predict heart disease which will help medical practitioners in diagnosing heart disease at early stages. The model consists of the rough sets based heart disease feature selection module and the fuzzy rule based classification module. The generated rules from fuzzy classifiers are optimized by applying the adaptive genetic algorithm. First, important features which effect heart disease are selected by rough set theory. The second step predicts the heart disease using the hybrid AGAFL classifier. The experimentation is performed on the publicly available UCI heart disease datasets. Thorough experimental analysis shows that our approach has outperformed current existing methods.


Disease classification Adaptive genetic algorithm Rough set theory Feature reduction Membership function 



  1. 1.
    Brameier M, Banzhaf W (2001) A comparison of linear genetic programming and neural networks in medical data mining. IEEE Trans Evol Comput 5(1):17–26CrossRefGoogle Scholar
  2. 2.
    Cios KJ (2000) From the guest editor medical data mining and knowledge discovery. IEEE Eng Med Biol Mag 19(4):15–16CrossRefGoogle Scholar
  3. 3.
    Clarkson K, Srivastava G, Meawad F, Dwivedi AD (2019) Where’s @waldo? Finding users on twitter. In: Artificial intelligence and soft computing—18th international conference, ICAISC 2019, Zakopane, Poland, June 16–20, 2019, proceedings, part II, pp 338–349. CrossRefGoogle Scholar
  4. 4.
    Dwivedi AD, Malina L, Dzurenda P, Srivastava G (2019) Optimized blockchain model for internet of things based healthcare applications. In: 42nd international conference on telecommunications and signal processing, TSP 2019, Budapest, Hungary, July 1–3, 2019, pp 135–139.
  5. 5.
    Dwivedi AD, Srivastava G, Dhar S, Singh R (2019) A decentralized privacy-preserving healthcare blockchain for IOT. Sensors 19(2):326. CrossRefGoogle Scholar
  6. 6.
    Feyyad U (1996) Data mining and knowledge discovery: making sense out of data. IEEE Expert 11(5):20–25CrossRefGoogle Scholar
  7. 7.
    Fisher R (1955) Statistical methods and scientific induction. J R Stat Soc Series B Stat Methodol 17(1):69–78MathSciNetzbMATHGoogle Scholar
  8. 8.
    Game PS, Vaze V, Emmanuel M (2019) Optimized decision tree rules using divergence based grey wolf optimization for big data classification in health care. Evol Intel. CrossRefGoogle Scholar
  9. 9.
    Gandomi AH, Yang XS, Alavi AH (2013) Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng Comput 29(1):17–35CrossRefGoogle Scholar
  10. 10.
    Han J, Rodriguez JC, Beheshti M (2008) Diabetes data analysis and prediction model discovery using rapidminer. In: 2008 second international conference on future generation communication and networking, vol. 3. IEEE, pp 96–99Google Scholar
  11. 11.
    Henriques J, Carvalho P, Paredes S, Rocha T, Habetha J, Antunes M, Morais J (2014) Prediction of heart failure decompensation events by trend analysis of telemonitoring data. IEEE J Biomed Health Inform 19(5):1757–1769CrossRefGoogle Scholar
  12. 12.
    Herland M, Khoshgoftaar TM, Wald R (2014) A review of data mining using big data in health informatics. J Big Data 1(1):2CrossRefGoogle Scholar
  13. 13.
    Kaluri R, Reddy P (2016) Sign gesture recognition using modified region growing algorithm and adaptive genetic fuzzy classifier. Int J Intell Eng Syst 9:225–233Google Scholar
  14. 14.
    Kharat KD, Kulkarni PP, Nagori M (2012) Brain tumor classification using neural network based methods. Int J Comput Sci Inform 1(4):2231–5292Google Scholar
  15. 15.
    Lahsasna A, Ainon RN, Zainuddin R, Bulgiba A (2012) Design of a fuzzy-based decision support system for coronary heart disease diagnosis. J Med Syst 36(5):3293–3306CrossRefGoogle Scholar
  16. 16.
    Lehmann TM, Güld MO, Deselaers T, Keysers D, Schubert H, Spitzer K, Ney H, Wein BB (2005) Automatic categorization of medical images for content-based retrieval and data mining. Comput Med Imaging Graph 29(2–3):143–155CrossRefGoogle Scholar
  17. 17.
    Long NC, Meesad P, Unger H (2015) A highly accurate firefly based algorithm for heart disease prediction. Expert Syst Appl 42(21):8221–8231CrossRefGoogle Scholar
  18. 18.
    Mohan S, Thirumalai C, Srivastava G (2019) Effective heart disease prediction using hybrid machine learning techniques. IEEE Access 7:81542–81554. CrossRefGoogle Scholar
  19. 19.
    Pawlak Z, Sowinski R (1994) Rough set approach to multi-attribute decision analysis. Eur J Oper Res 72(3):443–459CrossRefGoogle Scholar
  20. 20.
    Reddy GT, Khare N (2017) An efficient system for heart disease prediction using hybrid ofbat with rule-based fuzzy logic model. J Circuits Systems Comput 26(04):1750061CrossRefGoogle Scholar
  21. 21.
    Reddy GT, Khare N (2018) Heart disease classification system using optimised fuzzy rule based algorithm. Int J Biomed Eng Technol 27(3):183–202CrossRefGoogle Scholar
  22. 22.
    Santhanam T, Ephzibah E (2015) Heart disease prediction using hybrid genetic fuzzy model. Indian J Sci Technol 8(9):797CrossRefGoogle Scholar
  23. 23.
    Seera M, Lim CP (2014) A hybrid intelligent system for medical data classification. Expert Syst Appl 41(5):2239–2249CrossRefGoogle Scholar
  24. 24.
    Si W, Srivastava G, Zhang Y, Jiang L (2019) Green internet of things application of a medical massage robot with system interruption. IEEE Access 7:127066–127077. CrossRefGoogle Scholar
  25. 25.
    Sidek KA, Mai V, Khalil I (2014) Data mining in mobile ecg based biometric identification. J Netw Comput Appl 44:83–91CrossRefGoogle Scholar
  26. 26.
    Srinivas K, Rao GR, Govardhan A (2014) Rough-fuzzy classifier: a system to predict the heart disease by blending two different set theories. Arab J Sci Eng 39(4):2857–2868CrossRefGoogle Scholar
  27. 27.
    Srivastava G, Crichigno J, Dhar S (2019) A light and secure healthcare blockchain for iot medical devices. In: 2019 IEEE Canadian conference of electrical and computer engineering (CCECE), pp 1–5.
  28. 28.
    Thippa Reddy G, Khare N (2016) FFBAT-optimized rule based fuzzy logic classifier for diabetes. Int J Eng Res Afr 24:137–152CrossRefGoogle Scholar
  29. 29.
    Tsymbal A, Bolshakova N (2006) Guest editorial introduction to the special section on mining biomedical data. IEEE Trans Inf Technol Biomed 10(3):425–428CrossRefGoogle Scholar
  30. 30.
    Vafaie M, Ataei M, Koofigar HR (2014) Heart diseases prediction based on ecg signals’ classification using a genetic-fuzzy system and dynamical model of ECG signals. Biomed Signal Process Control 14:291–296CrossRefGoogle Scholar
  31. 31.
    Wang P, Weise T, Chiong R (2011) Novel evolutionary algorithms for supervised classification problems: an experimental study. Evol Intell 4(1):3–16CrossRefGoogle Scholar
  32. 32.
    Yuvaraj N, Vivekanandan P (2013) An efficient SVM based tumor classification with symmetry non-negative matrix factorization using gene expression data. In: 2013 international conference on information communication and embedded systems (ICICES). IEEE, pp 761–768Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Vellore Institute of TechnologyVelloreIndia
  2. 2.Department of Mathematics and Computer ScienceBrandon UniversityBrandonCanada
  3. 3.Research Center for Interneural ComputingChina Medical UniversityTaichungTaiwan, ROC

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