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Estimating the Time-Lapse Between Medical Insurance Reimbursement with Non-parametric Regression Models

  • Mary Akinyemi
  • Chika Yinka-BanjoEmail author
  • Ogban-Asuquo Ugot
  • Akwarandu Nwachuku
Conference paper
  • 18 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1130)

Abstract

Nonparametric supervised learning algorithms represent a succinct class of supervised learning algorithms where the learning parameters are highly flexible and whose values are directly dependent on the size of the training data. In this paper, we comparatively study the properties of four nonparametric algorithms, k-Nearest Neighbours (k-NNs), Support Vector Machines (SVMs), Decision trees and Random forests. The supervised learning task is a regression estimate of the time lapse in medical insurance reimbursement. Our study is concerned precisely with how well each of the nonparametric regression models fits the training data. We quantify the goodness of fit using the R-squared metric. The results are presented with a focus on the effect of the size of the training data, the feature space dimension and hyperparameter optimization. The findings suggest k-NN’s and SVM’s algorithms as better models in predicting well-defined output labels (i.e. Time lapse in days). However, overall, the decision tree model performs better because it makes a better prediction on new data points than the ballpark estimates made from likelihood models: SVMs and k-NNs.

Keywords

Machine learning Supervised learning Non-parametric learning 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Mary Akinyemi
    • 1
  • Chika Yinka-Banjo
    • 1
    Email author
  • Ogban-Asuquo Ugot
    • 1
  • Akwarandu Nwachuku
    • 1
  1. 1.University of LagosLagosNigeria

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