Summarize this abstract in two sentences

Longitudinal data are essential in healthcare and medical applications, especially with the emergence of novel technologies like wearable devices and sensors. This paper introduces a novel methodology that addresses the challenge of jointly performing feature selection and classification of functional data in scenarios with categorical responses and longitudinal features. Our approach tackles a newly defined optimization problem that integrates logistic loss and functional features to identify the most crucial features for classification. To address the minimization procedure, we employ functional principal components and develop an innovative adaptive version of the Dual Augmented Lagrangian algorithm that leverages the sparsity structure of the problem for dimensionality reduction. The method’s computational efficiency enables handling high-dimensional scenarios where the number of features may considerably exceed the number of statistical units. The efficacy of the method is demonstrated through simulations and a real data application, analyzing relationships between four chronic diseases and others health and socio-demographic factors. Avoid using the following keywords: device, minimization, unit, sparsity
user