The Relationship Between Head Injury and Alzheimer's Disease: A Causal Analysis with Bayesian Networks

TL;DR

This paper investigates the potential causal link between head injury and Alzheimer's disease using Bayesian networks and regression models, finding no significant protective effect of head injury but emphasizing the importance of memory complaints.

Contribution

It applies Bayesian network analysis to explore causal relationships between medical history factors and Alzheimer's disease, providing new insights into their complex interactions.

Findings

Head injury shows no significant effect on AD risk.

Memory complaints are strongly associated with AD.

Bayesian networks help clarify causal relationships.

Abstract

This study examines the potential causal relationship between head injury and the risk of developing Alzheimer's disease (AD) using Bayesian networks and regression models. Using a dataset of 2,149 patients, we analyze key medical history variables, including head injury history, memory complaints, cardiovascular disease, and diabetes. Logistic regression results suggest an odds ratio of 0.88 for head injury, indicating a potential but statistically insignificant protective effect against AD. In contrast, memory complaints exhibit a strong association with AD, with an odds ratio of 4.59. Linear regression analysis further confirms the lack of statistical significance for head injury (coefficient: -0.0245, p = 0.469) while reinforcing the predictive importance of memory complaints. These findings highlight the complex interplay of medical history factors in AD risk assessment and underscore the need for further research utilizing larger datasets and advanced causal modeling techniques.