Interpretable Deep Learning for Biological Age Prediction: A Counterfactual Approach to Personalized Health Insights

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Bhavana Nare

Interpretable Deep Learning for Biological Age Prediction: A Counterfactual Approach to Personalized Health Insights

Authors: Bhavana Nare

Unique Paper ID: 182985
Volume: 12
Issue: no
PageNo: 46-65

Keywords: Deep Learning Time Series Extrinsic Regression Counterfactuals Explanations.

Abstract:
The accurate estimation of biological age from physical activity data has the potential to revolutionize personalized health monitoring and early disease detection. However, existing deep learning models often lack interpretability, limiting their practical application in real-world healthcare settings. In this study, we propose an Explainable Time Series Regression (XTSR) framework that integrates deep learning with counterfactual reasoning to enhance model transparency and user trust. Our approach employs a hybrid Time Series Extrinsic Regression (TSER) model, trained on large-scale wearable sensor data, to predict biological age while simultaneously generating counter- factual explanations. By identifying the most influential activity patterns contributing to aging predictions, our system offers actionable recommendations for personalized health optimization. Experimental results demonstrate that our model outperforms traditional regression methods, achieving higher accuracy and interpretability. This research bridges the gap between predictive analytics and human-centered AI, paving the way for intelligent and user-friendly health monitoring systems that provide action- able insights based on individual behavior patterns.

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Cite This Article

ISSN: 2349-6002
Volume: 12
Issue: no
PageNo: 46-65

Interpretable Deep Learning for Biological Age Prediction: A Counterfactual Approach to Personalized Health Insights

Available:https://ijirt.org/article?manuscript=182985

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