scholarly journals Optimisation and evaluation of the random forest model in the efficacy prediction of chemoradiotherapy for advanced cervical cancer based on radiomics signature from high-resolution T2 weighted images

2021 ◽  
Author(s):  
Defeng Liu ◽  
Xiaohang Zhang ◽  
Tao Zheng ◽  
Qinglei Shi ◽  
Yujie Cui ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Radiation Therapy ◽  
Random Forest ◽  
Neoadjuvant Chemotherapy ◽  
Decision Trees ◽  
Treatment Effect ◽  
Gini Index ◽  
Random Forest Model ◽  
Advanced Cervical Cancer ◽  
Forest Model

Abstract Purpose Our objective was to establish a random forest model and to evaluate its predictive capability of the treatment effect of neoadjuvant chemotherapy–radiation therapy. Methods This retrospective study included 82 patients with locally advanced cervical cancer who underwent scanning from March 2013 to May 2018. The random forest model was established and optimised based on the open source toolkit scikit-learn. Byoptimising of the number of decision trees in the random forest, the criteria for selecting the final partition index and the minimum number of samples partitioned by each node, the performance of random forest in the prediction of the treatment effect of neoadjuvant chemotherapy–radiation therapy on advanced cervical cancer (> IIb) was evaluated. Results The number of decision trees in the random forests influenced the model performance. When the number of decision trees was set to 10, 25, 40, 55, 70, 85 and 100, the performance of random forest model exhibited an increasing trend first and then a decreasing one. The criteria for the selection of final partition index showed significant effects on the generation of decision trees. The Gini index demonstrated a better effect compared with information gain index. The area under the receiver operating curve for Gini index attained a value of 0.917. Conclusion The random forest model showed potential in predicting the treatment effect of neoadjuvant chemotherapy–radiation therapy based on high-resolution T2WIs for advanced cervical cancer (> IIb).

scholarly journals Fault diagnosis method of submersible screw pump based on random forest

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0242458
Author(s):  
Minzheng Jiang ◽  
Tiancai Cheng ◽  
Kangxing Dong ◽  
Shufan Xu ◽  
Yulong Geng
Keyword(s):  
Fault Diagnosis ◽  
Random Forest ◽  
Decision Trees ◽  
Processing System ◽  
Random Forest Model ◽  
Categorical Variables ◽  
Oil Well ◽  
Screw Pump ◽  
Forest Model ◽  
Diagnosis Method

The difficulty in directly determining the failure mode of the submersible screw pump will shorten the life of the system and the normal production of the oil well. This thesis aims to identify the fault forms of submersible screw pump accurately and efficiently, and proposes a fault diagnosis method of the submersible screw pump based on random forest. HDFS storage system and MapReduce processing system are established based on Hadoop big data processing platform; Furthermore, the Bagging algorithm is used to collect the training set data. Also, this thesis adopts the CART method to establish the sample library and the decision trees for a random forest model. Six continuous variables, four categorical variables and fault categories of submersible screw pump oil production system are used for training the decision trees. As several decision trees constitute a random forest model, the parameters to be tested are input into the random forest models, and various types of decision trees are used to determine the failure category in the submersible screw pump. It has been verified that the accuracy rate of fault diagnosis is 92.86%. This thesis can provide some meaningful guidance for timely detection of the causes of downhole unit failures, reducing oil well production losses, and accelerating the promotion and application of submersible screw pumps in oil fields.

scholarly journals Detecting Myocardial Infarction by Electrocardiogram Machine Learning Models With Greater Accuracy; A Technical Advance Article

2021 ◽  
Author(s):  
M.D.S. Sudaraka ◽  
I. Abeyagunawardena ◽  
E. S. De Silva ◽  
S Abeyagunawardena
Keyword(s):  
Machine Learning ◽  
Myocardial Infarction ◽  
Random Forest ◽  
Decision Trees ◽  
Sensitivity And Specificity ◽  
Random Forest Model ◽  
Multi Layer Perceptron ◽  
Learning Models ◽  
Forest Model ◽  
Machine Learning Models

Abstract BackgroundElectrocardiogram (ECG) is a key diagnostic test in cardiac investigation. Interpretation of ECG is based on the understanding of normal electrical patterns produced by the heart and alterations of those patterns in specific disease conditions. With machine learning techniques, it is possible to interpret ECGs with increased accuracy. However, there is a lacuna in machine learning models to detect myocardial infarction (MI) coupled with the affected territories of the heart. MethodsThe dataset was obtained from the University of California, Irvine, Machine Learning Repository. It was filtered to obtain observations categorized as Normal, Ischemic changes, Old Anterior MI and Old Inferior MI. The dataset was randomly split into a training set (70%) and a test set (30%). 73 out of the 270 ECG features were selected based on the changes observed following MI, after excluding predictors that had near zero variance across the observations. Three machine learning classification models (Bootstrap Aggregation Decision Trees, Random Forest, Multi-layer Perceptron) were trained using the training dataset, optimizing for the Kappa statistic and the parameter tuning was achieved with repeated 10-fold cross validation. Accuracy and Kappa of the samples were used to evaluate performance between the models. ResultsThe Random Forest model identified old anterior and old inferior MIs with 100% sensitivity and specificity and all 4 categorized observations with an overall accuracy of 0.9167 (95% CI 0.8424 - 0.9633). Both the Bootstrap Aggregation Decision Trees and the Multi-layer Perceptron models identified old anterior MIs with 100% sensitivity and specificity and their overall accuracies for all 4 observations were 0.8958 (95% CI 0.8168 - 0.9489) and 0.8542 (95% CI 0.7674 - 0.9179) respectively.Conclusion With a medically informed feature selection we were able to identify old anterior MI with 100% sensitivity and specificity by all three models in this study, and old inferior MI with 100% sensitivity and specificity by Random Forest Model. If the data set can be improved it is possible to utilize these machine learning models in hospital setting to identify cardiac emergencies by incorporating them into cardiac monitors, until trained personnel become available.

THE ROLE OF SURGICAL STAGE IN COMBINATION THERAPY FOR PATIENTS WITH LOCALLY ADVANCED CERVICAL CANCER

2019 ◽  
Vol 65 (5) ◽  
pp. 749-755
Author(s):  
D. Reyes Santyago ◽  
Anzhella Khadzhimba ◽  
M. Smirnova ◽  
Sergey Maksimov
Keyword(s):  
Cervical Cancer ◽  
Radiation Therapy ◽  
Combination Therapy ◽  
Lymph Nodes ◽  
Locally Advanced ◽  
Advanced Cervical Cancer ◽  
Relapse Free Survival ◽  
Regional Lymph Nodes ◽  
Free Survival ◽  
Locally Advanced Cervical Cancer

Objective: to justify the expediency of the surgical stage as a part of the combination treatment for stage IIA-IIIB cervical cancer. Materials and methods. The study included 343 women with stage IIA-IIIB cervical cancer treated from 2013 to 2016 with mandatory follow-up for at least 2 years. Patients were divided into 2 groups. The first group included 214 patients who received a combination treatment. At the first stage, neoadjuvant chemoradiation therapy was performed (remote radiation therapy 5 days a week with radio modification with Cisplatin once a week at a dose of 40 mg/m2). After evaluating the effect, patients were subjected to surgical treatment or continued chemoradiotherapy. The second group (n = 129) received standard combined radiation therapy. Various schemes of combination and complex treatment and standard combined radiation therapy were evaluated using the indices of general and relapse-free survival. Results. The proposed scheme for the combination therapy for patients with locally advanced cervical cancer showed significantly higher survival rates at all the analyzed stages. For the combined treatment group with complete cytoreduction, the two-year overall and relapse-free survival with stage IIA is 94.1% vs. 82.4%, with IIB 90.8% vs. 80.3%, with IIB 87.5% vs. 75%, with IIB with metastatic lesion of regional lymph nodes 85% vs. 70%. For the second group, two-year overall and relapse-free survival with stage IIA 75% vs. 50%, with IIB 70.9% vs. 56.3%, with IIB 59.1% vs. 40.9%, with IIB with metastatic lesion of regional lymph nodes 62.2% and 40.5%. The advantages of this approach are most clearly seen within patients with metastatic lesions of regional lymph nodes (85% vs. 62% accordingly). Conclusion. Cytoreductive surgery in combination with the combination therapy allows to achieve a significant increase in overall and relapse-free survival for patients with locally advanced cervical cancer compared with standard treatment programs.

scholarly journals Clinical trial registries as Scientometric data: A novel solution for linking and deduplicating clinical trials from multiple registries

2021 ◽  
Author(s):  
Christian Thiele ◽  
Gerrit Hirschfeld ◽  
Ruth von Brachel
Keyword(s):  
Clinical Trials ◽  
Random Forest ◽  
Random Forest Model ◽  
Scientometric Analysis ◽  
Data Set ◽  
The Public ◽  
Forest Model ◽  
Clinical Trial Registries ◽  
Multiple Primary ◽  
Clinical Trials Registry

AbstractRegistries of clinical trials are a potential source for scientometric analysis of medical research and serve important functions for the research community and the public at large. Clinical trials that recruit patients in Germany are usually registered in the German Clinical Trials Register (DRKS) or in international registries such as ClinicalTrials.gov. Furthermore, the International Clinical Trials Registry Platform (ICTRP) aggregates trials from multiple primary registries. We queried the DRKS, ClinicalTrials.gov, and the ICTRP for trials with a recruiting location in Germany. Trials that were registered in multiple registries were linked using the primary and secondary identifiers and a Random Forest model based on various similarity metrics. We identified 35,912 trials that were conducted in Germany. The majority of the trials was registered in multiple databases. 32,106 trials were linked using primary IDs, 26 were linked using a Random Forest model, and 10,537 internal duplicates on ICTRP were identified using the Random Forest model after finding pairs with matching primary or secondary IDs. In cross-validation, the Random Forest increased the F1-score from 96.4% to 97.1% compared to a linkage based solely on secondary IDs on a manually labelled data set. 28% of all trials were registered in the German DRKS. 54% of the trials on ClinicalTrials.gov, 43% of the trials on the DRKS and 56% of the trials on the ICTRP were pre-registered. The ratio of pre-registered studies and the ratio of studies that are registered in the DRKS increased over time.

scholarly journals Multi-Scenario Prediction of Intra-Urban Land Use Change Using a Cellular Automata-Random Forest Model

2021 ◽  
Vol 10 (8) ◽  
pp. 503
Author(s):  
Hang Liu ◽  
Riken Homma ◽  
Qiang Liu ◽  
Congying Fang
Keyword(s):  
Land Use ◽  
Random Forest ◽  
Cellular Automata ◽  
Land Use Change ◽  
Urban Land ◽  
Urban Land Use ◽  
Random Forest Model ◽  
Growth Trend ◽  
Related Factors ◽  
Forest Model

The simulation of future land use can provide decision support for urban planners and decision makers, which is important for sustainable urban development. Using a cellular automata-random forest model, we considered two scenarios to predict intra-land use changes in Kumamoto City from 2018 to 2030: an unconstrained development scenario, and a planning-constrained development scenario that considers disaster-related factors. The random forest was used to calculate the transition probabilities and the importance of driving factors, and cellular automata were used for future land use prediction. The results show that disaster-related factors greatly influence land vacancy, while urban planning factors are more important for medium high-rise residential, commercial, and public facilities. Under the unconstrained development scenario, urban land use tends towards spatially disordered growth in the total amount of steady growth, with the largest increase in low-rise residential areas. Under the planning-constrained development scenario that considers disaster-related factors, the urban land area will continue to grow, albeit slowly and with a compact growth trend. This study provides planners with information on the relevant trends in different scenarios of land use change in Kumamoto City. Furthermore, it provides a reference for Kumamoto City’s future post-disaster recovery and reconstruction planning.

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