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<p>Feature selection has emerged as a powerful tool for evaluating the relevance of variables in classification models. In high-dimensional datasets, not all features contribute meaningfully to accurate classification, and their inclusion can negatively affect model performance. Often, only a small subset of features is truly informative; therefore, identifying and removing irrelevant variables is crucial. In this paper, we propose a novel feature selection method based on logistic Generalized Additive Models (GAMs). Model performance was evaluated using the Bayesian Information Criterion (BIC) and the Area Under the ROC - Receiver Operating Characteristic- Curve (AUC). To demonstrate the effectiveness of this approach, we applied it to data from a clinical study aimed at diagnosing Type 2 Diabetes Mellitus (T2DM), distinguishing between diabetic and healthy individuals. The regression model incorporated features derived from Heart Period (HP) and Systolic Arterial Pressure (SAP) time series, as well as Baroreflex Sensitivity (BRS) measures, collected under two conditions: at rest and after active standing. The best-performing models selected between three and five covariates. Among the top 20 models, AUC values ranged from 0.931 to 0.918. The best 3-covariate model achieved a sensitivity of 0.816 with a false positive rate of 0.184. These results highlight the potential of this statistical methodology to enhance the discrimination between T2DM patients and control subjects.</p>

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A model-based feature selection approach for type 2 Diabetes Mellitus diagnosis using Heart Rate and Systolic Arterial Pressure series measures

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月6日

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  • Javier Roca-Pardiñas,
  • María J. Lado,
  • Leandro Rodríguez-Liñares

摘要

Feature selection has emerged as a powerful tool for evaluating the relevance of variables in classification models. In high-dimensional datasets, not all features contribute meaningfully to accurate classification, and their inclusion can negatively affect model performance. Often, only a small subset of features is truly informative; therefore, identifying and removing irrelevant variables is crucial. In this paper, we propose a novel feature selection method based on logistic Generalized Additive Models (GAMs). Model performance was evaluated using the Bayesian Information Criterion (BIC) and the Area Under the ROC - Receiver Operating Characteristic- Curve (AUC). To demonstrate the effectiveness of this approach, we applied it to data from a clinical study aimed at diagnosing Type 2 Diabetes Mellitus (T2DM), distinguishing between diabetic and healthy individuals. The regression model incorporated features derived from Heart Period (HP) and Systolic Arterial Pressure (SAP) time series, as well as Baroreflex Sensitivity (BRS) measures, collected under two conditions: at rest and after active standing. The best-performing models selected between three and five covariates. Among the top 20 models, AUC values ranged from 0.931 to 0.918. The best 3-covariate model achieved a sensitivity of 0.816 with a false positive rate of 0.184. These results highlight the potential of this statistical methodology to enhance the discrimination between T2DM patients and control subjects.