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记者来自中华民国陕西的新闻报道，研究表明：“性别判断是法医鉴定的一个关键方面。一些特殊的解剖结构，如上颌窦，在恶劣的环境下仍然可以保持完整性，可以作为性别判断的基础。”我们的新闻编辑引用了西安交通大学的一篇研究，“由于性别估计的复杂性，我们已经使用不同的机器学习算法进行了几项研究，以提高从解剖测量中预测性别的准确性。”本文利用锥束计算机断层扫描技术(CBCT)收集西北地区汉族人群马鼻窦的线性数据,并利用R 4.3.1. CBCT图像建立Logistic、K近邻或(KNN)、支持向量机(SVM)和随机森林(RF)模型,对477例汉族人群(男75例,女81例,年龄5~17岁,男162例,女159例,进行性别评价。以18~72岁年龄段(18~72岁)为研究对象,测量左、右上颌窦长度(MSL)、宽度(MSW)、高度(MSH)和上颌窦侧壁间距(距离),随机选取80%的数据作为训练集,其余数据作为测试集,并按年龄段分组,拟合模型作为尝试。18岁以上个体性别估计的准确率为77.78%，男性为78.12%，女性为77.42%，但18岁以下个体性别估计的准确性具有挑战性，与Logistic、KNN和SVM相比，RF具有更高的准确率。将年龄作为一个变量，提高了性别估计的准确性，特别是在18-27岁年龄组，准确率提高到88.46%。所有变量均与年龄呈线性相关，上颌窦的线性测量可作为18岁及以上人群性别估计的有用工具，本文建立了西北地区汉族人群性别估计的稳健RF模型。
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 Shaanxi, Peo ple’s Republic of China, by NewsRx correspondents, research stated, “Sex estimat ion is a critical aspect of forensic expertise. Some special anatomical structur es, such as the maxillary sinus, can still maintain integrity in harsh environme ntal conditions and may be served as a basis for sex estimation.” Our news editors obtained a quote from the research from Xi’an Jiaotong Universi ty, “Due to the complex nature of sex estimation, several studies have been cond ucted using different machine learning algorithms to improve the accuracy of sex prediction from anatomical measurements. In this study, linear data of the maxi llary sinus in the population of northwest China by using Cone-Beam Computed Tom ography (CBCT) were collected and utilized to develop logistic, K-Nearest Neighb or (KNN), Support Vector Machine (SVM) and random forest (RF) models for sex est imation with R 4.3.1. CBCT images from 477 samples of Han population (75 males a nd 81 females, aged 5-17 years; 162 males and 159 females, aged 18-72) were used to establish and verify the model. Length (MSL), width (MSW), height (MSH) of b oth the left and right maxillary sinuses and distance of lateral wall between tw o maxillary sinuses (distance) were measured. 80% of the data were randomly picked as the training set and others were testing set. Besides, these samples were grouped by age bracket and fitted models as an attempt. Overall, t he accuracy of the sex estimation for individuals over 18 years old on the testi ng set was 77.78%, with a slightly higher accuracy rate for males a t 78.12% compared to females at 77.42%. However, accu racy of sex estimation for individuals under 18 was challenging. In comparison t o logistic, KNN and SVM, RF exhibited higher accuracy rates. Moreover, incorpora ting age as a variable improved the accuracy of sex estimation, particularly in the 18-27 age group, where the accuracy rate increased to 88.46%. M eanwhile, all variables showed a linear correlation with age. The linear measure ments of the maxillary sinus could be a valuable tool for sex estimation in indi viduals aged 18 and over. A robust RF model has been developed for sex estimatio n within the Han population residing in the northwestern region of China.”
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