The fertility rate of China has shown an overall decline trend,so the fully understanding of the factors affecting China’s fertility intention has become the focus of the *** on CFPS2020 data,the study subjects were ...
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The fertility rate of China has shown an overall decline trend,so the fully understanding of the factors affecting China’s fertility intention has become the focus of the *** on CFPS2020 data,the study subjects were women of age between 20 and 49,who were born after the 1970s and were influenced by the family planning ***,SMOTE-catboost algorithm was used to construct a *** results show that:(1)For the women’s willingness to have multiple children,the SMOTE-catboost algorithm is more effective than the catboost algorithm,and the classification accuracy is improved by 8 percentage points.(2)The factors influencing women’s willingness to have multiple children were ranked by social status,intergenerational care,education level and *** with high social status are more willing to have multiple children;All-day intergenerational care has a positive effect on women’s willingness to have multiple *** women’s willingness to have multiple children declines with the rise of education level but increases with age.(3)Combined with the CFPS2018 data,the influencing factors changed over *** 2018,the important factors related to women’s willingness to have multiple children were mainly related to economic,within which the income ranked locally is the most important *** 2020,the most important factor changed to be social status while economic became less important.
Liver disease ranks as one of the leading causes of mortality globally, often going undetected until advanced stages. This study aims to enhance early detection of liver disease by employing machine learning models th...
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Liver disease ranks as one of the leading causes of mortality globally, often going undetected until advanced stages. This study aims to enhance early detection of liver disease by employing machine learning models that utilize key health indicators. Utilizing the Indian Liver Patient Dataset (ILPD) from the UCI repository, we developed a predictive model using the catboost algorithm, achieving an initial accuracy of 74%. To improve this, feature selection was performed using the Whale Optimization algorithm (WOA) and Harris Hawk Optimization (HHO), which increased accuracy to 82% and 85% respectively. The methodology involved preprocessing to correct data imbalances and outlier removal through univariate and bivariate analyses. These optimizations highlight the critical features enhancing the model's predictive capability. The results indicate that integrating metaheuristic algorithms in feature selection significantly improves the accuracy of liver disease prediction models. Future research could explore the integration of additional datasets and machine learning models to further refine predictive capabilities and understand the underlying pathophysiology of liver diseases.
作者：
Han ShuwenYang XiZhou QingZhuang JingWu WeiHuzhou Univ
Huzhou Cent Hosp Affiliated Cent Hosp Dept Oncol Huzhou Peoples R China Huzhou Univ
Huzhou Cent Hosp Affiliated Cent Hosp Dept Nursing Huzhou Peoples R China Huzhou Univ
Grad Sch Nursing Huzhou Peoples R China Huzhou Univ
Huzhou Cent Hosp Affiliated Cent Hosp Dept Gastroenterol 198 Hongqi Rd Huzhou 313000 Zhejiang Peoples R China
Background Early diagnosis of liver metastasis is of great importance for enhancing the survival of colorectal adenocarcinoma (CAD) patients, and the combined use of a single biomarker in a classier model has shown gr...
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Background Early diagnosis of liver metastasis is of great importance for enhancing the survival of colorectal adenocarcinoma (CAD) patients, and the combined use of a single biomarker in a classier model has shown great improvement in predicting the metastasis of several types of cancers. However, it is little reported for CAD. This study therefore aimed to screen an optimal classier model of CAD with liver metastasis and explore the metastatic mechanisms of genes when applying this classier model. Methods The differentially expressed genes between primary CAD samples and CAD with metastasis samples were screened from the Moffitt Cancer Center (MCC) dataset . The classification performances of six selected algorithms, namely, LR, RF, SVM, GBDT, NN, and catboost, for classification of CAD with liver metastasis samples were compared using the MCC dataset by detecting their classification test accuracy. In addition, the consortium datasets of and were used as internal and external validation sets to screen the optimal method. Subsequently, functional analyses and a drug-targeted network construction of the feature genes when applying the optimal method were conducted. Results The optimal catboost model with the highest accuracy of 99%, and an area under the curve of 1, was screened, which consisted of 33 feature genes. A functional analysis showed that the feature genes were closely associated with a "steroid metabolic process" and "lipoprotein particle receptor binding" (eg APOB and APOC3). In addition, the feature genes were significantly enriched in the "complement and coagulation cascade" pathways (eg FGA, F2, and F9). In a drug-target interaction network, F2 and F9 were predicted as targets of menadione. Conclusion The catboost model constructed using 33 feature genes showed the optimal classification performance for identifying CAD with liver metastasis.
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