What is the full form of CDKP

Design of Rheumatoid Arthritis Predictor Model Using Machine Learning Algorithms

tip

Access other chapters in this book by swiping

2018 | OriginalPaper | Book chapter

Abstract

The main aim of this paper is to investigate various data mining and machine learning techniques employed for the analysis of rheumatoid arthritis prediction based on clinical and genetic factors. The clinical characters and gene factors are collected from various hospitals in Coimbatore region through laboratory investigations from the blood serum samples and general investigations. Patients with viral fever more than six weeks and later arthritis affected compared with those patients with viral fever and no rheumatoid arthritis developed. This study involves detailed analysis of machine learning algorithms employed for rheumatoid arthritis disease, and genetic factors involved in this disease. The relevant attributes taken from the literature and consultation of rheumatologists, a combination of clinical and genetic factors evolved in this disease. The proposed model works in a big data environment named Machine Learning based Ensemble Analytic Approach (MLEAA) consists of two phases, namely learning phase and prediction phase. In learning phase data’s are processed by map reduce framework in hadoop and the featured attributes are working towards prediction phase. The proposed MLEAA approach prediction phase consists of three different algorithms, namely Ababoost, SVM, ANN and based on voting system final predictive value is calculated. From this study achieve better results and it will be very useful for predict rheumatoid arthritis earlier.

Would you like to get access to this content? Then find out more about our products now:

Springer Professional "Business + Technology"

With Springer Professional "Business + Technology" you get access to:

  • above 69,000 books
  • above 500 magazines

from the following fields:

  • Automobile + engines
  • Construction + real estate
  • Business IT + informatics
  • Electrical engineering + electronics
  • Energy + environment
  • Finance + Banking
  • Management + leadership
  • Marketing + sales
  • Mechanical engineering + materials
  • Insurance + risk

Try now for 30 days free of charge.

Springer Professional "Technology"

With Springer Professional "Technology" you get access to:

  • above 50,000 books
  • above 380 magazines

from the following fields:

  • Automobile + engines
  • Construction + real estate
  • Business IT + informatics
  • Electrical engineering + electronics
  • Energy + environment
  • Mechanical engineering + materials



Try now for 30 days free of charge.

Springer Professional "Economy"

With Springer Professional "Economy" you get access to:

  • above 58,000 books
  • above 300 magazines

from the following fields:

  • Construction + real estate
  • Business IT + informatics
  • Finance + Banking
  • Management + leadership
  • Marketing + sales
  • Insurance + risk



Try now for 30 days free of charge.

literature
Go back to reference Mohan, Vasanth Konda, Ganesan, Nalini, and Rajasekhar, Gopalakrishnan. 2014. Association of Susceptible Genetic Markers and Autoantibodies in Rheumatoid Arthritis. Journal of Genetics 93 (2): 597-605. Mohan, Vasanth Konda, Ganesan, Nalini, and Rajasekhar, Gopalakrishnan. 2014. Association of Susceptible Genetic Markers and Autoantibodies in Rheumatoid Arthritis. Journal of Genetics 93 (2): 597-605.
Go back to reference Bridges Jr., S. Louis, and Robert P. Kimberly. 2002. Genetic Influences on Treatment Response in Rheumatoid Arthritis. In Modern Therapeutics in Rheumatic Diseases, ed. G.C. Tsokos, et al. Totowa, NJ: Humana Press Inc. Bridges Jr., S. Louis, and Robert P. Kimberly. 2002. Genetic Influences on Treatment Response in Rheumatoid Arthritis. In Modern Therapeutics in Rheumatic Diseases, ed. G.C. Tsokos, et al. Totowa, NJ: Humana Press Inc.
Go back to reference Vanja, Paunic, Michael, Steinbach, Vipin, Kumar, Martin, Maiers. 2012. Prediction of HLA Genes from SNP Data and HLA Haplotype Frequencies. In 2012 IEEE 12th International Conference on Data Mining Workshops. Vanja, Paunic, Michael, Steinbach, Vipin, Kumar, Martin, Maiers. 2012. Prediction of HLA Genes from SNP Data and HLA Haplotype Frequencies. In 2012 IEEE 12th International Conference on Data Mining Workshops.
Go back to reference Yang, Peng, Xiaoli, Li, Hon-Nian, Chua, Chee-Keong, Kwoh, See-Kiong, Ng. 2014. Ensemble Positive Unlabeled Learning for Disease Gene Identification. PlusOne. https: // doi. org / 10. 1371 / journal. pone. 0097079. Yang, Peng, Xiaoli, Li, Hon-Nian, Chua, Chee-Keong, Kwoh, See-Kiong, Ng. 2014. Ensemble Positive Unlabeled Learning for Disease Gene Identification. PlusOne. https: // doi. org / 10. 1371 / journal. pone. 0097079.
Go back to reference Zahra, Shiezadeh, Hedieh, Sajedi and Elham Aflakie. 2015. Diagnosis of Rheumatoid Arthritis Using an Ensemble Learning Approach, 139-148. ICAITA, SAI, CDKP, Signal, NCO-2015. Zahra, Shiezadeh, Hedieh, Sajedi and Elham Aflakie. 2015. Diagnosis of Rheumatoid Arthritis Using an Ensemble Learning Approach, 139-148. ICAITA, SAI, CDKP, Signal, NCO-2015.
About this chapter
title
Design of Rheumatoid Arthritis Predictor Model Using Machine Learning Algorithms
DOI
https://doi.org/10.1007/978-981-10-6698-6_7
Authors:
S. Shanmugam
J. Preethi
publishing company
Springer Singapore

premium partner