摘要
机器人与机器学习的新闻编辑每日新闻-机器学习的新研究是一篇报道的主题。根据NewsRx记者从加拿大多伦多发回的新闻报道，研究表明：“胎盘植入谱(PAS)是一种产科疾病，由胎盘异常粘附于子宫壁引起，经常导致危及生命的并发症，包括产后出血。尽管它很重要，但在分娩前PAS仍然经常被低估。”我们的新闻编辑从研究中获得了一句话：“本研究深入到机器学习的领域，以提高PAS分类的精度，我们提出了两种不同的基于超声纹理特征的PAS分类模型，第一种模型利用机器学习技术，利用从超声扫描中提取的文本特征，第二种模型采用线性C分类器。”利用“加权Z-分数”的综合特征。我们的方法的一个新方面是将经典的机器学习和基于统计的特征选择方法相结合。这再加上基于定量图像特征的更跨父分类模型，与传统的机器学习方法相比，我们的线性AR分类器和机器学习模型的测试准确率分别为87%和92%。和5倍交叉验证准确度分别为88.7(4.4)和83.0(5.0)。提出的模型说明了增强PAS检测的实用和稳健工具的有效性，提供了非侵入性和切除效率高的诊断工具。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting originating from Toronto, Can ada, by NewsRx correspondents, research stated, “Placenta accreta spectrum (PAS) is an obstetric disorder arising from the abnormal adherence of the placenta to the uterine wall, often leading to life-threatening complications including pos tpartum hemorrhage. Despite its significance, PAS remains frequently underdiagno sed before delivery.” Our news editors obtained a quote from the research, “This study delves into the realm of machine learning to enhance the precision of PAS classification. We in troduce two distinct models for PAS classification employing ultrasound texture features. The first model leverages machine learning techniques, harnessing text ure features extracted from ultrasound scans. The second model adopts a linear c lassifier, utilizing integrated features derived from ‘weighted z-scores’. A nov el aspect of our approach is the amalgamation of classical machine learning and statistical-based methods for feature selection. This, coupled with a more trans parent classification model based on quantitative image features, results in sup erior performance compared to conventional machine learning approaches. Our line ar classifier and machine learning models attain test accuracies of 87 % and 92 %, and 5-fold cross validation accuracies of 88.7 (4.4) and 83.0 (5.0), respectively. The proposed models illustrate the effectiveness of pr actical and robust tools for enhanced PAS detection, offering non-invasive and c omputationally-efficient diagnostic tools.”
NETL
NSTL