Prediction of Prognosis and Survival of Patients with Gastric Cancer by Weighted Improved Random Forest Model

Xu, Cheng; Wang, Jing; Zheng, Tianlong; Cao, Yue; Ye, Fan

doi:10.5114/aoms/135594

Cited by 9 publications

(7 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Substitute the data of table x into equations ( 12) and ( 13) successively, and substitute the zik data calculated according to the construction model into equation (24).…”

Section: Competition Chart Methods To Evaluate the Scientific And Tec...mentioning

confidence: 99%

Evaluation of Scientific and Technological Innovation Ability of Free Trade Zone Based on Random Forest Weighting Method

Meng

2022

Mobile Information Systems

View full text Add to dashboard Cite

The scientific and technological innovation ability of the economic free trade zone is crucial to the depth and breadth of its economic development. There are too many subjective factors in the evaluation of the scientific and technological innovation ability of traditional economic free trade zones. In order to objectively evaluate the scientific and technological innovation ability of the free trade zone, this paper uses the random forest weighting method and the weighted linear combination to construct the evaluation index system, designs the evaluation model of the scientific and technological innovation ability of the free trade zone, and makes a specific analysis and evaluation based on the operation data of the economic and technological innovation ability of China’s four key free trade zones in 2020. The results show that the scientific and technological innovation ability of Guangdong economic free trade zone is the strongest, followed by Guangdong economic and trade zone, Shanghai economic and trade zone, Zhejiang economic and trade zone, and Tianjin economic and trade zone; Guangdong free trade zone has strong scientific and technological innovation ability. Compared with other free trade zones, Guangdong’s main advantages lie in the integration and aggregation ability of the industrial chain, strong policy support, and talent attraction. The scientific and technological innovation ability forms a virtuous circle. The analysis of the model example shows that the introduction of the random forest weighting method into the scientific and technological evaluation of the free trade zone can more objectively compare and analyze the scientific and technological innovation ability of the free trade zone, which is of great significance to help the free trade zone find out the problems and shortcomings in the scientific and technological innovation ability and improve the level of scientific and technological innovation.

show abstract

“…Substitute the data of table x into equations ( 12) and ( 13) successively, and substitute the zik data calculated according to the construction model into equation (24).…”

Section: Competition Chart Methods To Evaluate the Scientific And Tec...mentioning

confidence: 99%

Evaluation of Scientific and Technological Innovation Ability of Free Trade Zone Based on Random Forest Weighting Method

Meng

2022

Mobile Information Systems

View full text Add to dashboard Cite

show abstract

“…In particular, the RF model showed the best performance compared with the other models. The RF algorithm uses a number of decision trees and predicts more accurately by averaging the data in case of regression and voting them in case of classification 31 . The RF algorithm can also be used with a wide range of sample sizes including small sample sizes.…”

Section: Discussionmentioning

confidence: 99%

A novel CT-based radiomics model for predicting response and prognosis of chemoradiotherapy in esophageal squamous cell carcinoma

Kasai,

Miyoshi,

Sato

et al. 2024

Sci Rep

View full text Add to dashboard Cite

No clinically relevant biomarker has been identified for predicting the response of esophageal squamous cell carcinoma (ESCC) to chemoradiotherapy (CRT). Herein, we established a CT-based radiomics model with artificial intelligence (AI) to predict the response and prognosis of CRT in ESCC. A total of 44 ESCC patients (stage I-IV) were enrolled in this study; training (n = 27) and validation (n = 17) cohorts. First, we extracted a total of 476 radiomics features from three-dimensional CT images of cancer lesions in training cohort, selected 110 features associated with the CRT response by ROC analysis (AUC ≥ 0.7) and identified 12 independent features, excluding correlated features by Pearson’s correlation analysis (r ≥ 0.7). Based on the 12 features, we constructed 5 prediction models of different machine learning algorithms (Random Forest (RF), Ridge Regression, Naive Bayes, Support Vector Machine, and Artificial Neural Network models). Among those, the RF model showed the highest AUC in the training cohort (0.99 [95%CI 0.86–1.00]) as well as in the validation cohort (0.92 [95%CI 0.71–0.99]) to predict the CRT response. Additionally, Kaplan-Meyer analysis of the validation cohort and all the patient data showed significantly longer progression-free and overall survival in the high-prediction score group compared with the low-prediction score group in the RF model. Univariate and multivariate analyses revealed that the radiomics prediction score and lymph node metastasis were independent prognostic biomarkers for CRT of ESCC. In conclusion, we have developed a CT-based radiomics model using AI, which may have the potential to predict the CRT response as well as the prognosis for ESCC patients with non-invasiveness and cost-effectiveness.

show abstract

“…ML refers to a wide range of algorithms that can make predictions that mimic human decisions and represents a major form of artificial intelligence [ 50 ]. Cutting-edge computer technologies of this kind have been widely used in the healthcare field and have achieved remarkable results, such as the use of artificial intelligence image recognition technology to diagnose multiple malignant tumor patients accurately through medical images [ 51 – 53 ] and the use of ML to predict the prognosis and survival of patients with malignant tumors [ 27 , 54 ]. However, some uncertainty exists about the diagnostic efficacy [ 55 ].…”

Section: Discussionmentioning

confidence: 99%

Gut mycobiome as a potential non-invasive tool in early detection of lung adenocarcinoma: a cross-sectional study

Liu,

Zhang,

Pei

et al. 2023

BMC Med

View full text Add to dashboard Cite

Background The gut mycobiome of patients with lung adenocarcinoma (LUAD) remains unexplored. This study aimed to characterize the gut mycobiome in patients with LUAD and evaluate the potential of gut fungi as non-invasive biomarkers for early diagnosis. Methods In total, 299 fecal samples from Beijing, Suzhou, and Hainan were collected prospectively. Using internal transcribed spacer 2 sequencing, we profiled the gut mycobiome. Five supervised machine learning algorithms were trained on fungal signatures to build an optimized prediction model for LUAD in a discovery cohort comprising 105 patients with LUAD and 61 healthy controls (HCs) from Beijing. Validation cohorts from Beijing, Suzhou, and Hainan comprising 44, 17, and 15 patients with LUAD and 26, 19, and 12 HCs, respectively, were used to evaluate efficacy. Results Fungal biodiversity and richness increased in patients with LUAD. At the phylum level, the abundance of Ascomycota decreased, while that of Basidiomycota increased in patients with LUAD. Candida and Saccharomyces were the dominant genera, with a reduction in Candida and an increase in Saccharomyces, Aspergillus, and Apiotrichum in patients with LUAD. Nineteen operational taxonomic unit markers were selected, and excellent performance in predicting LUAD was achieved (area under the curve (AUC) = 0.9350) using a random forest model with outcomes superior to those of four other algorithms. The AUCs of the Beijing, Suzhou, and Hainan validation cohorts were 0.9538, 0.9628, and 0.8833, respectively. Conclusions For the first time, the gut fungal profiles of patients with LUAD were shown to represent potential non-invasive biomarkers for early-stage diagnosis.

show abstract

Prediction of Prognosis and Survival of Patients with Gastric Cancer by Weighted Improved Random Forest Model

Cited by 9 publications

References 20 publications

Evaluation of Scientific and Technological Innovation Ability of Free Trade Zone Based on Random Forest Weighting Method

Evaluation of Scientific and Technological Innovation Ability of Free Trade Zone Based on Random Forest Weighting Method

A novel CT-based radiomics model for predicting response and prognosis of chemoradiotherapy in esophageal squamous cell carcinoma

Gut mycobiome as a potential non-invasive tool in early detection of lung adenocarcinoma: a cross-sectional study

Contact Info

Product

Resources

About