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Early Prediction of Non-communicable Diseases Using Soft Computing Methodology

  • Ravindar MogiliEmail author
  • G. NarsimhaEmail author
  • Konda SrinivasEmail author
Conference paper
Part of the Learning and Analytics in Intelligent Systems book series (LAIS, volume 3)

Abstract

Even though non-communicable diseases (NCDs) are deadly diseases, the curing and survival rate of them can increase with early prediction. But identification of NCD in the early stage is difficult due to complex clinical attributes and genetic factors. This task can be simplified with the aid of data mining and soft computing techniques. Initially dataset is pre-processed to enhance data quality and then disease prediction model is developed with soft computing methods to identify the disease stage. Later association rules are generated after applying fuzzy clustering to predict the probability of getting the disease in the future and risk factors associated with it individual wise.

Keywords

Non-communicable diseases Early prediction SVM Neural network Fuzzy clustering Association rules Soft computing 

References

  1. 1.
    Global Health Estimates 2016: (2018) Disease burden by Cause, Age, Sex, by Country and by Region, 2000–2016. Geneva, World Health OrganizationGoogle Scholar
  2. 2.
    Khera AV et al (2018) Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat. Genet. 50(9):1219Google Scholar
  3. 3.
    Cinetha K, Dr. Uma Maheswari P, Mar.-Apr. (2014) Decision support system for precluding coronary heart disease using fuzzy logic. Int. J. Comput. Sci. Trends Technol. (IJCST) 2(2):102–107Google Scholar
  4. 4.
    Anderson KM et al (1991) Cardiovascular disease risk profiles. Am. Heart J. 293–298Google Scholar
  5. 5.
    Dolatabadi AD, Khadem SEZ, Asl BM (2017) Automated diagnosis of coronary artery disease (CAD) patients using optimized SVM. Comput. Methods Programs Biomed. 138:117–126Google Scholar
  6. 6.
    Kahramanli H, Allahverdi N (2008) Design of a hybrid system for the diabetes and heart diseases. Expert Syst. Appl. 35(1–2):82–89Google Scholar
  7. 7.
    Adalı T, Şekeroğlu B (2012) Analysis of micrornas by neural network for early detection of cancer. Procedia Technol. 1:449–452Google Scholar
  8. 8.
    Geman O, Chiuchisan I, Toderean R (2017) Application of adaptive neuro-fuzzy inference system for diabetes classification and prediction. E-health and bioengineering conference (EHB), 2017. IEEEGoogle Scholar
  9. 9.
    Troyanskaya O et al (2001) Missing value estimation methods for DNA microarrays. Bioinformatics 17(6):520–525Google Scholar
  10. 10.
    Cruz JA, Wishart DS (2006) Applications of machine learning in cancer prediction and prognosis. Cancer Inf. 2:117693510600200030Google Scholar
  11. 11.
    Krishnaiah V, Narsimha DG, Chandra DNS (2013) Diagnosis of lung cancer prediction system using data mining classification techniques. Int. J. Comput. Sci. Inf. Technol. 4(1):39–45Google Scholar
  12. 12.
    Cortes C, Vapnik V (1995) Support-vector networks. Mach. Learn. 20(3):273–297zbMATHGoogle Scholar
  13. 13.
    Asuncion A, Newman D (2007) UCI machine learning repositoryGoogle Scholar
  14. 14.
    Pedregosa F et al (2011) Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12(Oct):2825–2830Google Scholar
  15. 15.
    Peirce JW (2009) Generating stimuli for neuroscience using PsychoPy. Frontiers in Neuroinformatics 2:10Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of CSEJyothishmathi Institute of Technology and ScienceKarimnagarIndia
  2. 2.Department of CSEJawaharlal Nehru Technological UniversityHyderabadIndia
  3. 3.Department of CSECMR Technical CampusHyderabadIndia

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