Abstract：
The aim of this study was at building an effective machine learning model to contribute to the prediction of stroke recurrence in adult stroke patients subjected to moyamoya disease (MMD), while at analyzing the factors for stroke recurrence.
The data of this retrospective study originated from the database of JiangXi Province Medical Big Data Engineering & Technology Research Center. Moreover, the information of MMD patients admitted to the second affiliated hospital of Nanchang university from January 1st, 2007 to December 31st, 2019 was acquired. A total of 661 patients from January 1st, 2007 to February 28th, 2017 were covered in the training set, while the external validation set comprised 284 patients that fell into a scope from March 1st, 2017 to December 31st, 2019. First, the information regarding all the subjects was compared between the training set and the external validation set. The key influencing variables were screened out using the Lasso Regression Algorithm. Furthermore, the models for predicting stroke recurrence in 1, 2, and 3 years after the initial stroke were built based on five different machine learning algorithms, and all models were externally validated and then compared. Lastly, the CatBoost model with the optimal performance was explained using the SHapley Additive exPlanations (SHAP) interpretation model.
In general, 945 patients suffering from MMD were recruited, and the recurrence rate of acute stroke in 1, 2, and 3 years after the initial stroke reached 11.43%(108/945), 18.94%(179/945), and 23.17%(219/945), respectively. The CatBoost models exhibited the optimal prediction performance among all models; the area under the curve (AUC) of these models for predicting stroke recurrence in 1, 2, and 3 years was determined as 0.794 (0.787, 0.801), 0.813 (0.807, 0.818), and 0.789 (0.783, 0.795), respectively. As indicated by the results of the SHAP interpretation model, the high Suzuki stage, young adults (aged 18-44), no surgical treatment, and the presence of an aneurysm were likely to show significant correlations with the recurrence of stroke in adult stroke patients subjected to MMD.
In adult stroke patients suffering from MMD, the CatBoost model was confirmed to be effective in stroke recurrence prediction, yielding accurate and reliable prediction outcomes. High Suzuki stage, young adults (aged 18-44 years), no surgical treatment, and the presence of an aneurysm are likely to be significantly correlated with the recurrence of stroke in adult stroke patients subjected to MMD.
The data of this retrospective study originated from the database of JiangXi Province Medical Big Data Engineering & Technology Research Center. Moreover, the information of MMD patients admitted to the second affiliated hospital of Nanchang university from January 1st, 2007 to December 31st, 2019 was acquired. A total of 661 patients from January 1st, 2007 to February 28th, 2017 were covered in the training set, while the external validation set comprised 284 patients that fell into a scope from March 1st, 2017 to December 31st, 2019. First, the information regarding all the subjects was compared between the training set and the external validation set. The key influencing variables were screened out using the Lasso Regression Algorithm. Furthermore, the models for predicting stroke recurrence in 1, 2, and 3 years after the initial stroke were built based on five different machine learning algorithms, and all models were externally validated and then compared. Lastly, the CatBoost model with the optimal performance was explained using the SHapley Additive exPlanations (SHAP) interpretation model.
In general, 945 patients suffering from MMD were recruited, and the recurrence rate of acute stroke in 1, 2, and 3 years after the initial stroke reached 11.43%(108/945), 18.94%(179/945), and 23.17%(219/945), respectively. The CatBoost models exhibited the optimal prediction performance among all models; the area under the curve (AUC) of these models for predicting stroke recurrence in 1, 2, and 3 years was determined as 0.794 (0.787, 0.801), 0.813 (0.807, 0.818), and 0.789 (0.783, 0.795), respectively. As indicated by the results of the SHAP interpretation model, the high Suzuki stage, young adults (aged 18-44), no surgical treatment, and the presence of an aneurysm were likely to show significant correlations with the recurrence of stroke in adult stroke patients subjected to MMD.
In adult stroke patients suffering from MMD, the CatBoost model was confirmed to be effective in stroke recurrence prediction, yielding accurate and reliable prediction outcomes. High Suzuki stage, young adults (aged 18-44 years), no surgical treatment, and the presence of an aneurysm are likely to be significantly correlated with the recurrence of stroke in adult stroke patients subjected to MMD.
摘要：
目的：这项研究的目的是建立一个有效的机器学习模型，以帮助预测患有烟雾病（MMD）的成年卒中患者的卒中复发。同时分析中风复发的因素。
方法：本回顾性研究数据来源于江西省医疗大数据工程技术研究中心数据库。此外，南昌大学第二附属医院1月1日起收治的MMD患者信息,2007年12月31日,2019年被收购。1月1日共有661名患者,2007年2月28日,2017年被涵盖在培训集中，而外部验证集由284名患者组成，这些患者从3月1日起进入范围，2017年12月31日,2019.首先,在训练集和外部验证集之间比较了所有受试者的信息.使用Lasso回归算法筛选出关键影响变量。此外，基于五种不同的机器学习算法，建立了预测卒中后1年、2年和3年卒中复发的模型，所有模型都经过外部验证，然后进行比较。最后,使用Shapley加法扩张（SHAP）解释模型解释了具有最佳性能的CatBoost模型。
结果：一般来说，招募了945名患有MMD的患者，首次卒中后1年、2年和3年的急性卒中复发率达到11.43％（108/945），18.94%（179/945），和23.17%（219/945），分别。CatBoost模型在所有模型中表现出最佳的预测性能；这些模型预测1年、2年和3年中风复发的曲线下面积（AUC）被确定为0.794（0.787，0.801），0.813(0.807,0.818),和0.789（0.783，0.795），分别。如SHAP解释模型的结果表明，铃木的舞台，年轻人（18-44岁），没有手术治疗,在接受MMD治疗的成年卒中患者中,动脉瘤的存在可能与卒中复发显著相关.
结论：在患有MMD的成年中风患者中，CatBoost模型被证实在中风复发预测中有效，产生准确可靠的预测结果。高铃木舞台，年轻人（18-44岁），没有手术治疗,在接受MMD治疗的成年卒中患者中,动脉瘤的存在可能与卒中复发显著相关.
方法：本回顾性研究数据来源于江西省医疗大数据工程技术研究中心数据库。此外，南昌大学第二附属医院1月1日起收治的MMD患者信息,2007年12月31日,2019年被收购。1月1日共有661名患者,2007年2月28日,2017年被涵盖在培训集中，而外部验证集由284名患者组成，这些患者从3月1日起进入范围，2017年12月31日,2019.首先,在训练集和外部验证集之间比较了所有受试者的信息.使用Lasso回归算法筛选出关键影响变量。此外，基于五种不同的机器学习算法，建立了预测卒中后1年、2年和3年卒中复发的模型，所有模型都经过外部验证，然后进行比较。最后,使用Shapley加法扩张（SHAP）解释模型解释了具有最佳性能的CatBoost模型。
结果：一般来说，招募了945名患有MMD的患者，首次卒中后1年、2年和3年的急性卒中复发率达到11.43％（108/945），18.94%（179/945），和23.17%（219/945），分别。CatBoost模型在所有模型中表现出最佳的预测性能；这些模型预测1年、2年和3年中风复发的曲线下面积（AUC）被确定为0.794（0.787，0.801），0.813(0.807,0.818),和0.789（0.783，0.795），分别。如SHAP解释模型的结果表明，铃木的舞台，年轻人（18-44岁），没有手术治疗,在接受MMD治疗的成年卒中患者中,动脉瘤的存在可能与卒中复发显著相关.
结论：在患有MMD的成年中风患者中，CatBoost模型被证实在中风复发预测中有效，产生准确可靠的预测结果。高铃木舞台，年轻人（18-44岁），没有手术治疗,在接受MMD治疗的成年卒中患者中,动脉瘤的存在可能与卒中复发显著相关.
