Multivariate Regression Analysis for Coronary Heart Disease Using SPM Tool

Authors(1) :-Amudavalli L

Regression is a data mining (machine learning) technique used to fit an equation to a dataset. In statistical modeling, regression analysis is a statistical process for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables (or 'predictors'). More specifically, regression analysis helps one understand how the typical value of the dependent variable (or 'criterion variable') changes when any one of the independent variables is varied, while the other independent variables are held fixed. Most commonly, regression analysis estimates the conditional expectation of the dependent variable given the independent variables – that is, the average value of the dependent variable when the independent variables are fixed. Then can implement multivariate attributes in coronary heart disease datasets. In regression, analyze the multivariate attribute model which is a generalization of the probit model used to estimate several correlated binary outcomes jointly. In this project, perform comparative study to various regression algorithms such as CART, Ensemble and bagger, Random forest and MARS. Classification and regression trees (CART), Ensemble and bagger, Random forest for tree prediction models can be implemented in prediction model for heart disease datasets. And also propose the MARS procedure which builds flexible regression models by fitting separate splines for multiple predicted variables. Finally compare the results in terms of RMSE, MSE, MAD and gain values metrics in data mining.

Authors and Affiliations

Amudavalli L
M.Phil Scholar, Department of Computer Science, Jamal Mohamed College, Trichy, India

Regression Analysis, Multivariate Attribute, Dependent Variable, Classification And Regression Tree, Ensemble And Bagger, Random Forest

  1. Zhang, Wengang, and Anthony Teck Chee Goh. "Nonlinear structural modeling using multivariate adaptive regression splines." (2015).
  2. Leathwick, J. R., et al. "Using multivariate adaptive regression splines to predict the distributions of New Zealand's freshwater diadromous fish." Freshwater Biology 50.12 (2005): 2034-2052.
  3. Han, Qiuyi, et al. "SLANTS: Sequential Adaptive Nonlinear Modeling of Vector Time Series." arXiv preprint arXiv:1610.02725 (2016).
  4. Lu, Chi-Jie, Tian-Shyug Lee, and Chia-Mei Lian. "Sales forecasting for computer wholesalers: A comparison of multivariate adaptive regression splines and artificial neural networks." Decision Support Systems 54.1 (2012): 584-596.
  5. Paciorek, Christopher J., and Mark J. Schervish. "Nonstationary covariance functions for Gaussian process regression." Advances in neural information processing systems. 2004.
  6. Rossel, RA Viscarra, and Thorsten Behrens. "Using data mining to model and interpret soil diffuse reflectance spectra." Geoderma 158.1 (2010): 46-54.
  7. Menon, Ramkumar, et al. "Multivariate adaptive regression splines analysis to predict biomarkers of spontaneous preterm birth." Acta obstetricia et gynecologica Scandinavica 93.4 (2014): 382-391.
  8. Emamgolizadeh, S., et al. "Estimation of soil cation exchange capacity using genetic expression programming (GEP) and multivariate adaptive regression splines (MARS)." Journal of Hydrology 529 (2015): 1590-1600.
  9. Zhang, Wengang, et al. "Assessment of soil liquefaction based on capacity energy concept and multivariate adaptive regression splines." Engineering Geology 188 (2015): 29-37.
  10. Zakeri, Issa F., et al. "Cross-sectional time series and multivariate adaptive regression splines models using accelerometry and heart rate predict energy expenditure of preschoolers." The Journal of nutrition 143.1 (2013): 114-122.

Publication Details

Published in : Volume 2 | Issue 6 | November-December 2017
Date of Publication : 2017-12-31
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 330-338
Manuscript Number : CSEIT1725220
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Amudavalli L, "Multivariate Regression Analysis for Coronary Heart Disease Using SPM Tool", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 6, pp.330-338, November-December-2017. |          | BibTeX | RIS | CSV

Article Preview