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A decision support system for osteoporosis risk prediction using machine learning and explainable artificial

Varada Vivek Khanna¹, Krishnaraj Chadaga², Niranjana Sampathila¹

¹Department of Biomedical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, India.

|December 25, 2023

Summary

Machine learning models can predict osteoporosis risk with 89% accuracy using patient data. This system aims to aid physicians in early diagnosis and automated screening for this common bone condition.

Keywords:

Ensemble-learning Explainable machine learning Feature selection techniques Machine learning Osteoporosis

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Area of Science:

Medical Informatics
Artificial Intelligence in Healthcare
Bone Metabolism Research

Background:

Osteoporosis is a metabolic bone disease characterized by decreased bone mineral density and mass, leading to fragility.
The condition is often asymptomatic and undiagnosed until a fracture occurs, highlighting the need for early detection.
Machine learning (ML) offers powerful tools for analyzing health data to predict disease risk and improve patient outcomes.

Purpose of the Study:

To develop and evaluate heterogeneous machine learning frameworks for predicting osteoporosis risk.
To identify key patient parameters for accurate osteoporosis risk assessment.
To create an automated screening system to support clinical decision-making.

Main Methods:

Utilized an open-source dataset of 1493 patients including bone density, blood, and physical test results.
Applied thirteen distinct feature selection techniques to identify salient predictive parameters.
Developed a multi-level ensemble learning stack, optimized with Forward Feature Selection, for risk prediction.

Main Results:

The best-performing ML pipeline achieved an accuracy of 89% in predicting osteoporosis risk.
Explainable AI tools (SHAP, LIME, ELI5, Qlattice) were employed to ensure transparency and interpretability of model predictions.
The study successfully identified key features contributing to osteoporosis risk assessment.

Conclusions:

The developed ML frameworks provide a holistic approach to osteoporosis risk prediction.
The system offers potential for automated screening, assisting physicians in timely diagnosis.
Integrating ML and explainable AI can significantly enhance the management of osteoporosis.