首页|Xi’an Jiaotong University Reports Findings in Machine Learning (Different machin e learning methods based on maxillary sinus in sex estimation for northwestern C hinese Han population)
Xi’an Jiaotong University Reports Findings in Machine Learning (Different machin e learning methods based on maxillary sinus in sex estimation for northwestern C hinese Han population)
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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.”
ShaanxiPeople’s Republic of ChinaAsi aCyborgsEmerging TechnologiesMachine Learning
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