首页|Henan University Reports Findings in Machine Learning (Using machine learning mo dels to identify the risk of depression in middleaged and older adults with fre quent and infrequent nicotine use: A cross-sectional study)
Henan University Reports Findings in Machine Learning (Using machine learning mo dels to identify the risk of depression in middleaged and older adults with fre quent and infrequent nicotine use: A cross-sectional study)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
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 out of Kaifeng, People’s Repu blic of China, by NewsRx editors, research stated, “Depression is very prevalent in middle-aged and older smokers. Therefore, we aimed to identify the risk of d epression among middle-aged and older adults with frequent and infrequent nicoti ne use, as this is quite necessary for supporting their well-being.” Our news journalists obtained a quote from the research from Henan University, “ This study included a total of 10,821 participants, which were derived from the China Health and Retirement Longitudinal Study Wave 5, 2020 (CHARLS-5). Five mac hine learning (ML) algorithms were employed. Some metrics were used to evaluate the performance of models, including area under the receiver operating character istic curve (AUC), positive predictive value (PPV), specificity, accuracy. 10,82 1 participants (6472 males, 4349 females) had a mean age of 60.47 ± 8.98, with a score of 8.90 ± 6.53 on depression scale. For middle-aged and older adults with frequent nicotine use, random forest (RF) achieved the highest AUC value, PPV a nd specificity (0.75, 0.74 and 0.88, respectively). For the other group, support vector machines (SVM) showed the highest PPV (0.74), and relatively high accura cy and specificity (0.72 and 0.87, respectively). Feature importance analysis in dicated that ‘dissatisfaction with life’ was the most important variable of iden tifying the risk of depression in the SVM model, while ‘attitude towards expecte d life span’ was the most important one in the RF model. CHARLS-5 was collected during the COVID-19, so our results may be influenced by the pandemic.”
KaifengPeople’s Republic of ChinaAsi aCyborgsEmerging TechnologiesEpidemiologyMachine LearningRisk and Prev ention
NETL
NSTL
