Trustworthy Chronic Disease Risk Prediction For Self-Directed Preventive Care via Medical Literature Validation

TL;DR

This paper presents deep learning models for predicting 13 chronic diseases using personal and lifestyle data, validated against medical literature to ensure trustworthiness and explainability for self-directed preventive care.

Contribution

It introduces a novel validation approach that aligns model explanations with established medical literature, enhancing trust in self-assessment risk prediction models.

Findings

Strong alignment between model features and medical literature across 13 diseases.

Models enable accessible, self-directed risk assessment without medical tests.

Validation approach improves confidence in machine learning for preventive healthcare.

Abstract

Chronic diseases are long-term, manageable, yet typically incurable conditions, highlighting the need for effective preventive strategies. Machine learning has been widely used to assess individual risk for chronic diseases. However, many models rely on medical test data (e.g. blood results, glucose levels), which limits their utility for proactive self-assessment. Additionally, to gain public trust, machine learning models should be explainable and transparent. Although some research on self-assessment machine learning models includes explainability, their explanations are not validated against established medical literature, reducing confidence in their reliability. To address these issues, we develop deep learning models that predict the risk of developing 13 chronic diseases using only personal and lifestyle factors, enabling accessible, self-directed preventive care. Importantly, we use SHAP-based explainability to identify the most influential model features and validate them against established medical literature. Our results show a strong alignment between the models' most influential features and established medical literature, reinforcing the models' trustworthiness. Critically, we find that this observation holds across 13 distinct diseases, indicating that this machine learning approach can be broadly trusted for chronic disease prediction. This work lays the foundation for developing trustworthy machine learning tools for self-directed preventive care. Future research can explore other approaches for models' trustworthiness and discuss how the models can be used ethically and responsibly.