Akmese Omer F, Dogan Gul, Kor Hakan, Erbay Hasan, Demir Emre
Department of Computer Technologies, University of Hitit, University of Kırıkkale, Çorum 19500, Turkey.
Department of Surgical Medical Sciences, University of Hitit, Çorum 19040, Turkey.
Emerg Med Int. 2020 Apr 25;2020:7306435. doi: 10.1155/2020/7306435. eCollection 2020.
Acute appendicitis is one of the most common emergency diseases in general surgery clinics. It is more common, especially between the ages of 10 and 30 years. Additionally, approximately 7% of the entire population is diagnosed with acute appendicitis at some time in their lives and requires surgery. The study aims to develop an easy, fast, and accurate estimation method for early acute appendicitis diagnosis using machine learning algorithms. Retrospective clinical records were analyzed with predictive data mining models. The predictive success of the models obtained by various machine learning algorithms was compared. A total of 595 clinical records were used in the study, including 348 males (58.49%) and 247 females (41.51%). It was found that the gradient boosted trees algorithm achieves the best success with an accurate prediction success of 95.31%. In this study, an estimation method based on machine learning was developed to identify individuals with acute appendicitis. It is thought that this method will benefit patients with signs of appendicitis, especially in emergency departments in hospitals.
急性阑尾炎是普通外科门诊最常见的急诊疾病之一。它较为常见，尤其在10至30岁之间。此外，约7%的总人口在其一生中的某个时候会被诊断为急性阑尾炎并需要进行手术。该研究旨在使用机器学习算法开发一种简单、快速且准确的早期急性阑尾炎诊断估计方法。利用预测性数据挖掘模型对回顾性临床记录进行了分析。比较了各种机器学习算法所获得模型的预测成功率。该研究共使用了595份临床记录，其中男性348例（58.49%），女性247例（41.51%）。结果发现，梯度提升树算法取得了最佳效果，准确预测成功率为95.31%。在本研究中，开发了一种基于机器学习的估计方法来识别患有急性阑尾炎的个体。人们认为这种方法将使患有阑尾炎症状的患者受益，特别是在医院的急诊科。
