###### Abstract  
The relationship between risk factors for de novo hygroma in patients with traumatic brain injury (TBI) was investigated. We collected data on 222 patients with TBI to determine the risk factors for de novo hygroma, including sex, age, centrum semiovelaricular space (CSO-PVS) grade, trauma cause, hypertension, and diabetes. The importance of the risk factors was analyzed, and the feature contribution of the risk factors to all patients and each patient was analyzed using predictive modeling. Additionally, association rule mining was performed to determine the relationship between all factors, and the performance metrics of the predictive model were calculated. The overall feature importance was analyzed in the order of age, CSO-PVS, hypertension, and trauma cause. However, trauma cause, underlying disease, age, and sex as risk factors were different for a specific patient through the individual feature analysis. The mean area under the curve for the predictive model was 0.80 \(\pm\) 0.04 using K-fold cross validation. We analyzed the risk factors for de novo hygroma in TBI and identified detailed relationships. Age and CSO-PVS severity were strongly correlated with de novo hygroma. Furthermore, according to the results of feature importance analysis and association rule mining, the significance of the risk factors may vary in each individual patient. CSO-PVS; hygroma; risk factor analysis; explainable predictive modeling; association rule mining +
Footnote †: journal: Medical  
fluid flows into the subdural space through the arachnoid rupture and is then formed in isolation [10; 11]. In contrast, perivascular spaces (PVS) are fluid structures that surround small blood vessels in the brain [12]. PVS is an important factor in the mechanism of brain injuries [13; 14; 15]. As a result of analyzing risk factors in patients with mild TBI, it was reported that newly developed hygroma and enlarged centrum semiovale (CSO)-PVS are associated [3]. [PERSON] et al. (2015) reported predictive modeling results for the risk factors for TBI in children using machine learning [16]. In cases of 39 and 156 age-matched controls, the road traffic accident, a history of loss of consciousness, vomiting, and signs of base of skull fracture were found to be the four important risk factors (AUC = 0.98). [PERSON] et al. (2019) developed a simple and scalable machine learning-based algorithm that can predict mortality in real time during intensive care after traumatic brain injury [17]. Using a dataset of 472 patients, machine learning-based logistic regression modeling was developed, and 30-day mortality was predicted using variables based on intracranial pressure (ICP), mean arterial pressure (MAP), cerebral perfusion pressure (CPP), and Glasgow Coma Scale (GCS). As a results, the study distinguished survivors from non-survivors with up to 81% and 84% accuracy based on key variables. The receiver operating characteristic curve (AUC) increased from 0.67 on day 1 to 0.81 on day 5. In addition, a study that predicted the mortality of TBI patients was reported by [PERSON] et al. (2021) [18]. Although patient prognostic factors (GCS) were used in machine learning for mortality prediction, there is a limitation to identifying causal relationships between risk factors. However, machine learning studies on the de novo of subdural hygroma and related risk factors in TBI patients have not been reported. Since subdural hygroma is a cause of serious complications in patients with TBI, a substantial approach to the association of risk factors related to patient outcome prediction is required. Moreover, a predictive model study is required for a patient-specific prediction for each patient. In this study, we analyzed the importance of risk factors for de novo hygroma, including age, CSO-PVS, and underlying disease, in patients with mild TBI and the risk factors for individual patients using artificial intelligence techniques.