Estimating the time-lapse between medical insurance reimbursement with non-parametric regression models

TL;DR

This study compares four non-parametric regression algorithms—KNN, SVM, decision trees, and random forests—for estimating medical insurance reimbursement time-lapses, analyzing their fit quality based on data size, feature complexity, and hyperparameters.

Contribution

It provides a comparative analysis of non-parametric regression models in a medical reimbursement context, highlighting their performance factors.

Findings

Random forests achieved the highest R-squared scores.

Model performance improved with larger training datasets.

Hyperparameter tuning significantly affected model accuracy.

Abstract

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.