Causal Analysis of Bone Disease Risk Factors Based on Markov Boundary Discovery

Bone diseases, affecting millions globally, necessitate a nuanced understanding of their underlying causal mechanisms for effective prevention and treatment. Traditional machine learning and deep learning methods, while powerful in identifying patterns and associations, often fall short in distinguishing correlation from causation. This paper addresses this gap by employing the Markov boundary discovery algorithm, a novel causal inference technique, to analyze risk factors associated with bone diseases. Utilizing a comprehensive dataset of patient information, including the occurrence of bone diseases and related factors, we identify the direct causes of bone disease presence, providing a clear and concise depiction of the causal relationships at play. Our results demonstrate a strong alignment with expert opinions, validating the efficacy of our approach and its potential to revolutionize bone disease research. By bridging the gap between data-driven insights and clinical expertise, this study contributes to the enhanced understanding of bone disease etiology, the development of targeted interventions, and the improvement of patient outcomes, marking a significant stride in the field of biomedical research.