Estimating the time-lapse between medical insurance reimbursement with non-parametric regression models
This work addresses a domain-specific problem for healthcare or insurance stakeholders, but it is incremental as it applies existing methods to a new dataset without introducing novel algorithmic improvements.
The paper tackled the problem of estimating the time-lapse for medical insurance reimbursement by comparing four non-parametric regression models (KNN, SVM, Decision Trees, Random Forests), resulting in a quantification of their goodness-of-fit using the R-squared metric, with results focusing on training data size, feature dimension, and hyperparameter effects.
Non-parametric 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 (KNNs), 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.