Genetic algorithms with self-adaptation for predictive classification of Medicare standardized payments for physical therapists

Abstract

We present a self-adaptive genetic algorithm for the problem of predicting if a Medicare standardized payment to a physical therapist will be above or below the national median. The percentage of Americans 65 and over is expected to increase in the coming years, increasing the need for physical therapy services. As a result, accurate prediction of expected Medicare payments based on local factors will be of increasing importance. A self-adaptive genetic algorithm is an evolutionary algorithm in which some or all of the algorithm’s parameters are evolved over the course of its execution. Self-adaptation is a useful tool both for improving the performance of evolutionary algorithms, as well as improving usability through lessening the amount of parameter tuning required of the algorithm’s user. While other self-adaptive approaches tend to focus on self-adaptation of only a few parameters, our approach self-adapts all of the parameters related to crossover and mutation. We compare the performance of our self-adaptive genetic algorithm with that of logistic regression and a canonical genetic algorithm on the problem of predicting Medicare payments. Logistic regression is a commonly used benchmark for this type of problem and a canonical genetic algorithm is included to allow us to see if any performance costs arise from the self-adaptive mechanisms. Results show that our self-adaptive genetic algorithm is effective at the classification of Medicare standardized payments to physical therapists, achieving accuracies of over 93%. Performance remains strong with training sets as small as 5% of the full data set. The problem representation used by our method allows for the identification of the relevant features for classification which means that our approach is capable of simultaneously performing classification and feature selection.