AI-Enabled Diagnosis of Spontaneous Rupture of Ovarian Endometriomas: A PSO Enhanced Random Forest Approach

• Published in: IEEE access Vol. 8; pp. 132253 - 132264
• Main Authors: Zhou, Mingyan, Lin, Feng, Hu, Qian, Tang, Zhenzhou, Jin, Chu
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial intelligence; Classification; Clinical diagnosis; Diagnosis; Hemorrhaging; Machine learning; Mathematical models; Model accuracy; Ovarian endometrioma; Ovaries; Parameters; Particle swarm optimization; Predictive models; Protozoa; Radio frequency; random forest; Random forests; Rupturing; spontaneous rupture; Swarm intelligence; Training
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3008473

Spontaneous rupture of ovarian endometriomas (OEs) may cause serious injury to patients. However, in traditional clinical diagnosis, it is vulnerable to be ignored or misdiagnosed for the symptoms of the acute abdomen caused by it are quite similar to those of some common gynecological emergencies, which leads to serious complications to patients. In view of this, this study investigates the AI-enabled, early and accurate diagnosis of spontaneous rupture of OEs. Although artificial intelligence (AI) has been proved to be a powerful tool to help to make accurate clinical diagnosis, however, as far as we know, there is so far no report on AI-enabled diagnosis of spontaneous rupture of OEs yet. Specifically, this study proposes a particle swarm optimization (PSO) enhanced random forest (RF) classification model, called PSO-RF, to make diagnosis, where RF is used to rank feature importance and make diagnosis considering that an OE is ruptured or not is a typical 0-1 classification problem, and PSO is leveraged to fine-tune the essential parameters of RF. The performance of the proposed PSO-RF model is evaluated with practical data collected from a local hospital and fully benchmarked by comparing with eight other machine learning models whose key parameters are sufficiently optimized as well by grid search or PSO, for the sake of fairness. The experiment results show that the proposed PSO-RF model outperforms all the other models, with the accuracy of 97.47%, the area under the ROC curve (AUC) of 0.996, the sensitivity of 94.12% and the specificity of 98.39%. It can be concluded that the PSO-RF model is a highly effective AI-enabled tool for preoperatively diagnosing spontaneous rupture of OEs.