Age estimation based on forced expiratory spirometry

Abstract

This paper examines the benefits of forced expiratory spirometry (FES) test with powerful machine learning algorithms for the purpose age estimation. The proposed method consists of three phases: feature extraction, training of regression models and estimation. Some useful features are determined and extracted from the results of FES test in the first phase. In the second phase, the regression models based on Gaussian Mixture Models (GMM) and Support Vector Machine (SVM) are trained with available training data, and in the final stage, the age of the unknown individuals are estimated by means of trained models. All of the experiments are conducted with a large dataset of 4571 subjects to illustrate the performance gains obtained by the proposed algorithms The average absolute error between the true and the estimated age is 6.54 ± 4.9 (mean ± std.) using GMM method and 6.35 ± 4.7 using DVM method.