Analysis of the Risk Factors of Heart Disease Using Step-Wise Regression with Statistical Evaluation

  • S. K. Harsheni
  • S. Souganthika
  • K. Gokul Karthik
  • A. Sheik AbdullahEmail author
  • S. Selvakumar
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 35)


The objective of the work aims to formulate a regression model to predict the occurrence of the heart disease using minimum number of parameters. The problem of heart disease is chosen owing to the increasing risk of heart disease in India. The Data is collected from a hospital (Cleveland Dataset) which consists of 22 attributes and a class label as retrieved from UCI Repository. Technique of Step wise regression is used to formulate this model and this enables us to identify the model of the highest accuracy of about 89.72% that contains the attributes which have the highest effect on the class label (outcome variable). The technique of supervised learning is used to train and obtain the mathematical model. The observed result is an expression function value of the selected attributes that corresponds to the determination of heart disease with fewer set of attribute with its parametric values. Thereby, the number of tests that corresponds to the disease can be reduced which then reduces the expenses towards the disease and its co-morbidities.


Heart disease Predictive analysis Step-wise regression Statistical analysis Biomedical informatics 


  1. 1.
    Han, J., Kamber, M.: Data Mining Concepts and Techniques. Elsevier, India (2006)zbMATHGoogle Scholar
  2. 2.
    Global data on visual impairments 2010. World Health Organization, Geneva (2012)Google Scholar
  3. 3.
    Mendez, G.F., Cowie, M.R.: The epidemiological features of heart failure in developing countries: a review of the literature. Int. J. Cardiol. 80, 213–219 (2001)CrossRefGoogle Scholar
  4. 4.
    Global burden of disease—2004 update: World Health Organization, Geneva (2004)Google Scholar
  5. 5.
    Huffman, M.D., Prabhakaran, D.: Heart failure: epidemiology and prevention in India. Nat. Med. J. India 23(5), 283 (2010)Google Scholar
  6. 6.
    Bioch, J.C., Meer, O., Potharst, R.: Classification using Bayesian neural nets. In: International Conference on Neural Networks, pp. 1488–1493 (1996)Google Scholar
  7. 7.
    Patil, B.M., Joshi, R.C., Durga, T.: Hybrid prediction model for type-2 diabetic patients. Expert Syst. Appl. Sci. Direct 37(12), 8102–8108 (2010)CrossRefGoogle Scholar
  8. 8.
    Han, J., Rodriguez, J.C., Beheshti, M.: Diabetes data analysis and prediction model discovery using rapid miner. In: IEEE 2nd International Conference on Future Generation Communication and Networking, pp. 96–99 (2008)Google Scholar
  9. 9.
    Whittingham, M.J., Stephens, P.A., Bradbury, R.B., Freckleton, R.P.: Why do we still use step-wise modelling in ecology and behaviour? J. Anim. Ecol. 75, 1182–1189 (2006)CrossRefGoogle Scholar
  10. 10.
    Vivekanandan, T., Sriman Narayana Iyengar, N.Ch.: Optimal feature selection using a modified differential evolution algorithm and its effectiveness for prediction of heart disease. Comput. Biol. Med. 90(1), 125–136 (2017). Scholar
  11. 11.
    Amin, M.S., Chiam, Y.K., Varathan, K.D.: Identification of significant features and data mining techniques in predicting heart disease. Telematics Inform. 36, 82–93 (2019)CrossRefGoogle Scholar
  12. 12.
    Sheik Abdullah, A., Selvakumar, S., Parkavi, R., Suganya, R., Venkatesh, M.: An introduction to survival analytics, types, and its applications. Biomechanics. Intech Open Publishers, UK (2019).
  13. 13.
    Sheik Abdullah, A., Selvakumar, S., Karthikeyan, P., Venkatesh, M.: Comparing the efficacy of decision tree and its variants using medical data. Indian J. Sci. Technol. 10(18), 1–8 (2017). Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • S. K. Harsheni
    • 1
  • S. Souganthika
    • 1
  • K. Gokul Karthik
    • 1
  • A. Sheik Abdullah
    • 1
    Email author
  • S. Selvakumar
    • 2
  1. 1.Department of Information TechnologyThiagarajar College of EngineeringMaduraiIndia
  2. 2.Department of Computer Science and EngineeringG.K.M. College of Engineering and TechnologyChennaiIndia

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