Objective
To create a model for prediction of success of uterine‐preserving procedures in women with placenta accreta spectrum (PAS).
Methods
PAS‐ID is a multicenter study that included 11 centers from 9 countries. Women with PAS, who were managed between January 1, 2010 and December 31, 2019, were retrospectively included. Data were split into model development and validation cohorts, and a prediction model was created using logistic regression. Main outcome was success of uterine preservation.
Results
Out of 797 women with PAS, 587 were eligible. Uterus‐preserving procedures were successful in 469 patients (79.9%). Number of previous cesarean sections (CS) was inversely associated with management success (adjusted odds ratio [aOR] 0.02, 95% confidence interval [CI] 0.001–3.63 with five previous CS). Other variables were complete placental invasion (aOR 0.14, 95% CI 0.05–0.43), type of CS incision (aOR 0.04, 95% CI 0.01–0.25 for classical incision), compression sutures (aOR 2.48, 95% CI 1.00–6.16), accreta type (aOR 3.76, 95% CI 1.13–12.53), incising away from placenta (aOR 5.09, 95% CI 1.52–16.97), and uterine resection (aOR 102.57, 95% CI 3.97–2652.74).
Conclusion
The present study provides a prediction model for success of uterine preservation, which may assist preoperative and intraoperative decisions, and promote incorporation of uterine preservation procedures in comprehensive PAS protocols.
Nowadays, Internet of Things (IoT) technology has various network applications and has attracted the interest of many research and industrial communities. Particularly, the number of vulnerable or unprotected IoT devices has drastically increased, along with the amount of suspicious activity, such as IoT botnet and large-scale cyber-attacks. In order to address this security issue, researchers have deployed machine and deep learning methods to detect attacks targeting compromised IoT devices. Despite these efforts, developing an efficient and effective attack detection approach for resource-constrained IoT devices remains a challenging task for the security research community. In this paper, we propose an efficient and effective IoT botnet attack detection approach. The proposed approach relies on a Fisher-score-based feature selection method along with a genetic-based extreme gradient boosting (GXGBoost) model in order to determine the most relevant features and to detect IoT botnet attacks. The Fisher score is a representative filter-based feature selection method used to determine significant features and discard irrelevant features through the minimization of intra-class distance and the maximization of inter-class distance. On the other hand, GXGBoost is an optimal and effective model, used to classify the IoT botnet attacks. Several experiments were conducted on a public botnet dataset of IoT devices. The evaluation results obtained using holdout and 10-fold cross-validation techniques showed that the proposed approach had a high detection rate using only three out of the 115 data traffic features and improved the overall performance of the IoT botnet attack detection process.
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