Skip to main content
Springer Nature Link
Log in

Green Computing Process and its Optimization Using Machine Learning Algorithm in Healthcare Sector

  • Published:
Mobile Networks and Applications Aims and scope Submit manuscript

Abstract

Handling the information is crucial task in healthcare sector; the data mining techniques will be right choice to address the complex problems. The hybridized optimization techniques in big data analytics consider the important part of the healthcare network communication issues in decision making approach of patient information. This article focused on heart disease data mining and relevant issues since the heart diseases are considered as a reason for causing deaths just as for males and females all over the world. So, people need to be conscious of possible aspects of heart disease. Even though genetics has a part, some of the standards of living practiced are the fundamental reasons for the heart disease. The heart diseases are classified by classical techniques with 13 risk factors and helpful variables. The introduced approach delivers a new computing hybrid modeling scheme for detect the heart diseases. This study represents, various existing methods making decisions for cardio vascular risks depends on the artificial neural networks (ANN). This ANN based methods generally anticipated that Heart Failure attributes having same risk involvement to the heart failure diagnosis. In this article the strategy of an effective recognition method is analyzed for analyzing the failure related to heart diseases using a hybridized approach of K-Nearest Neighbor clustering and Spiral optimization in the classification of the cardio vascular risks. The hybridized KNN technique is matched with some data mining techniques like Support vector Machine (SVM), Convolutional Neural Networks (CNN), and Artificial Neural Networks (ANN). The experimental results of this work achieved optimized improved results significantly than other machine learning techniques. The illustrative results exposed that the hybrid scheme stated effectually classify heart disease in the way of computing optimized prediction of heart diseases. Overall the proposed algorithm evidence 5% of enhancement in prediction of heart diseases with comparison of other existing machine learning techniques.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Dwivedi AK (2016) Performance evaluation of different machine learning techniques for prediction of heart disease. Neural Comput & Applic 12:1–9

    Google Scholar 

  2. Patidar S, Pachori RB, Rajendra Acharya U (2015) Automated diagnosis of coronary artery disease using tunable-Q wavelet transform applied on heart rate signals. Knowl-Based Syst 82:1–10

    Article  Google Scholar 

  3. Sanz JA, Galar M, Jurio A, Brugos A, Pagola M, Bustince H (2014) Medical diagnosis of cardiovascular diseases using an interval-valued fuzzy rule-based classification system. Appl Soft Comput 20:103–111

    Article  Google Scholar 

  4. Acharya U, Rajendra KSV, Ghista DN, Lim WJE, Molinari F, Sankaranarayanan M (2015) Computer-aided diagnosis of diabetic subjects by heart rate variability signals using discrete wavelet transform method. Knowl-Based Syst 81:56–64

    Article  Google Scholar 

  5. Bashir S, Qamar U, Khan FH (2015) BagMOOV: a novel ensemble for heart disease prediction bootstrap aggregation with multi-objective optimized voting. Australas Phys Eng Sci Med 2:305–323

    Article  Google Scholar 

  6. Sergi G, Veronese N, Fontana L, De Rui M, Bolzetta F, Zambon S, Corti M-C et al (2015) Pre-frailty and risk of cardiovascular disease in elderly men and women: the pro. VA study. J Am Coll Cardiol 10:976–983

    Article  Google Scholar 

  7. Shao YE, Hou C-D, Chiu C-C (2014) Hybrid intelligent modeling schemes for heart disease classification. Appl Soft Comput 14:47–52

    Article  Google Scholar 

  8. Acharya UR, Faust O, Vinitha S, Swapna G, Martis RJ, Kadri NA, Suri JS (2014) Linear and nonlinear analysis of normal and CAD-affected heart rate signals. Comput Methods Prog Biomed 1:55–68

    Article  Google Scholar 

  9. Samuel OW, Asogbon GM, Sangaiah AK, Fang P, Li G (2017) An integrated decision support system based on ANN and Fuzzy_AHP for heart failure risk prediction. Expert Syst Appl 68:163–172

    Article  Google Scholar 

  10. Sabahi F (2018) Bimodal fuzzy analytic hierarchy process (BFAHP) for coronary heart disease risk assessment. J Biomed Inform 83:204–216

    Article  Google Scholar 

  11. Uyar K, İlhan A (2017) Diagnosis of heart disease using genetic algorithm based trained recurrent fuzzy neural networks. Procedia Comput Sci 120:588–593

    Article  Google Scholar 

  12. Tayefi M, Tajfard M, Saffar S, Hanachi P, Amirabadizadeh AR, Esmaeily H, Taghipour A, Ferns GA, Moohebati M, Ghayour-Mobarhan M (2017) Hs-CRP is strongly associated with coronary heart disease (CHD): a data mining approach using decision tree algorithm. Comput Methods Prog Biomed 141:105–109

    Article  Google Scholar 

  13. Joshi, Sujata, and Mydhili K. Nair. "Prediction of heart disease using classification based data mining techniques”. In Computational Intelligence in Data Mining Springer, New Delhi 2, (2015)503–511

  14. Anooj PK (2012) Clinical decision support system: risk level prediction of heart disease using weighted fuzzy rules. J King Saud Univ Comput Inf Sci 24(1):27–40

    Google Scholar 

  15. Kim HC, Greenland P, Rossouw JE, Manson JAE, Cochrane BB, Lasser NL, Limacher MC, Lloyd-Jones DM, Margolis KL, Robinson JG (2010) Multimarker prediction of coronary heart disease risk: the Women's Health Initiative. J Am Coll Cardiol 55(19):2080–2091

    Article  Google Scholar 

  16. Ouwerkerk W, Voors AA, Zwinderman AH (2014) Factors influencing the predictive power of models for predicting mortality and/or heart failure hospitalization in patients with heart failure. JACC: Heart Fail 2(5):429–436

    Google Scholar 

  17. Chandel K, Kunwar V, Sabitha S, Choudhury T, Mukherjee S (2016) A comparative study on thyroid disease detection using K-nearest neighbor and naive Bayes classification techniques. CSI Trans ICT 4(2–4):313–319

    Article  Google Scholar 

  18. Gao R, Yang Y, Han Y, Huo Y, Chen J, Yu B, Su X et al (2015) Bioresorbable vascular scaffolds versus metallic stents in patients with coronary artery disease: ABSORB China trial. J Am Coll Cardiol 66(21):2298–2309

    Article  Google Scholar 

  19. Fleisher LA, Fleischmann KE, Auerbach AD, Barnason SA, Beckman JA, Bozkurt B, Davila-Roman VG et al (2014) ACC/AHA guideline on perioperative cardiovascular evaluation and management of patients undergoing noncardiac surgery: a report of the American College of Cardiology/American Heart Association task force on practice guidelines. J Am Coll Cardiol 64(22):e77–e137

    Article  Google Scholar 

  20. Nørgaard BL, Leipsic J, Gaur S, Seneviratne S, Ko BS, Ito H, Jensen JM et al (2014) Diagnostic performance of noninvasive fractional flow reserve derived from coronary computed tomography angiography in suspected coronary artery disease: the NXT trial (Analysis of Coronary Blood Flow Using CT Angiography: Next Steps). J Am Coll Cardiol 63(12):1145–1155

    Article  Google Scholar 

  21. Park J, Bhuiyan MZA, Kang M, Son J, Kang K (2018) Nearest neighbor search with locally weighted linear regression for heartbeat classification. Soft Comput 22(4):1225–1236

    Article  Google Scholar 

  22. Benasla L, Belmadani A, Rahli M (2014) Spiral optimization algorithm for solving combined economic and emission dispatch. Int J Electr Power Energy Syst 62:163–174

    Article  Google Scholar 

  23. Kanj S, Abdallah F, Denœux T, Tout K (2016) Editing training data for multi-label classification with the k-nearest neighbor rule. Pattern Anal Applic 19(1):145–161

    Article  MathSciNet  Google Scholar 

  24. Acharya UR, Fujita H, Adam M, Lih OS, Sudarshan VK, Hong TJ, Koh JEW et al (2017) Automated characterization and classification of coronary artery disease and myocardial infarction by decomposition of ECG signals: a comparative study. Inf Sci 377:17–29

    Article  Google Scholar 

  25. Acharya UR, Fujita H, Sudarshan VK, Shu Lih O, Adam M, Tan JH, Koo JH, Jain A, Lim CM, Chua KC (2017) Automated characterization of coronary artery disease, myocardial infarction, and congestive heart failure using contourlet and shearlet transforms of electrocardiogram signal. Knowl-Based Syst 132:156–166

    Article  Google Scholar 

  26. Beritelli F, Capizzi G, Sciuto GL, Napoli C, Scaglione F (2018) Automatic heart activity diagnosis based on gram polynomials and probabilistic neural networks. Biomed Eng Lett 8(1):77–85

    Article  Google Scholar 

  27. Chen M, Hao Y, Hwang K, Lu W, Lin W (2017) Disease prediction by machine learning over big data from healthcare communities. IEEE Access 5:8869–8879

    Article  Google Scholar 

  28. Kumar SU, Inbarani HH (2017) Neighborhood rough set based ECG signal classification for diagnosis of cardiac diseases. Soft Comput 21(16):4721–4733

    Article  Google Scholar 

  29. Bashir S, Qamar U, Khan FH, Naseem L (2016) HMV: a medical decision support framework using multi-layer classifiers for disease prediction. J Comput Sci 13:10–25

    Article  Google Scholar 

  30. Beyan C, Fisher R (2015) Classifying imbalanced data sets using similarity based hierarchical decomposition. Pattern Recogn 48(5):1653–1672

    Article  Google Scholar 

  31. Shah SMS, Batool S, Khan I, Ashraf MU, Abbas SH, Hussain SA (2017) Feature extraction through parallel probabilistic principal component analysis for heart disease diagnosis. Physica A Stat Mech Appl 482:796–807

    Article  Google Scholar 

  32. Du M, Ding S, Jia H (2016) Study on density peaks clustering based on k-nearest neighbors and principal component analysis. Knowl-Based Syst 99:135–145

    Article  Google Scholar 

  33. Hassan N, Sayed OR, Khalil AM, Ghany MA (2017) Fuzzy Soft Expert System in Prediction of Coronary Artery Disease. Int J Fuzzy Syst 19(5):1546–1559

    Article  Google Scholar 

Download references

Acknowledgements

This project was funded by the Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah, under grant no. (DF-482-135-1441). The authors, therefore, acknowledge with thanks DSR for technical and financial support.

Author information

Authors and Affiliations

  1. Department of Industrial Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, Saudi Arabia

    A. H. Zubar

  2. Department of Mechanical Engineering, M.Kumarasamy College of Engineering, Karur, Tamilnadu, India

    R. Balamurugan

Authors
  1. A. H. Zubar
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. R. Balamurugan
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to A. H. Zubar.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zubar, A.H., Balamurugan, R. Green Computing Process and its Optimization Using Machine Learning Algorithm in Healthcare Sector. Mobile Netw Appl 25, 1307–1318 (2020). https://doi.org/10.1007/s11036-020-01549-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11036-020-01549-9

Keywords