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Association between body mass index and in vitro fertilization/intra-cytoplasmic sperm injection outcomes: An analysis of 15,124 normal ovarian responders in China

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中华医学杂志(英文版) 2024年第7期137卷 837-845页

作者： Zheng Danlei Wang Yuanyuan Chen Lixue Zeng Lin Li Rong Department of Obstetrics and Gynecology Center for Reproductive Medicine Peking University Third Hospital Beijing 100191 China Research Center of Clinical Epidemiology

Background: High body mass index (BMI) results in decreased fecundity, and women with high BMI have reduced rates of clinical pregnancy and live birth inin vitro fertilization/intra-cytoplasmic sperm injection (IVF/ICSI). Meanwhile, ovarian responses show great heterogeneity in patients with a high BMI. This study aimed to analyze the effects of a high BMI on IVF/ICSI outcomes in the Chinese female with normal ovarian response.Methods: We performed a retrospective cohort study comprising 15,124 patients from the medical record system of the Reproductive Center of Peking University Third Hospital, with 3530 (23.3%) in the overweight group and 1380 (9.1%) in the obese group, who had a normal ovarian response (5-15 oocytes retrieved) and underwent fresh embryo transfer (ET) cycles from January 2017 to December 2018, followed by linked frozen-thawed embryo transfer (FET) cycles from January 2017 to December 2020. Cumulative live birth rate (CLBR) was used as the primary outcome. Furthermore, a generalized additive model was applied to visually illustrate the curvilinear relationship between BMI and the outcomes. We used a decision tree to identify the specific population where high BMI had the greatest effect on IVF/ICSI outcomes.Results: High BMI was associated with poor IVF/ICSI outcomes, both in cumulative cycles and in separate fresh ET or FET cycles. In cumulative cycles, compared with the normal weight group, obesity was correlated with a lower positive pregnancy test rate (adjusted odds ratio [aOR]: 0.809, 95% confidence interval [CI]: 0.682-0.960), lower clinical pregnancy rate (aOR: 0.766, 95% CI: 0.646-0.907), lower live birth rate (aOR: 0.706, 95% CI: 0.595-0.838), higher cesarean section rate (aOR: 2.066, 95% CI: 1.533-2.785), and higher rate of large for gestational age (aOR: 2.273, 95% CI: 1.547-3.341). In the generalized additive model, we found that CLBR declined with increasing BMI, with 24 kg/m2 as an inflection point. In the decision tree, BMI only made a difference in the population aged ≤34.5 yea

关键词： Obesity Body mass index In vitro fertilization Live birth decision trees Normal ovarian response

Nitrogen removal influence factors in A/O process and decision trees for nitrification/denitrification system

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Journal of Environmental Sciences 2004年第6期16卷 901-907页

作者： MAYong PENGYong-zhen WANGShu-ying WANGXiao-lian SchoolofMunicipalandEnvironmentalEngineering HarbinInstituteofTechnologyHarbin150090China DepartmentofEnvironmentalEngineering BeijingUniversityofTechnologyBeijing100022China

In order to improve nitrogen removal in anoxic/oxic(A/O) process effectively for treating domestic wastewaters, the influence factors, DO(dissolved oxygen), nitrate recirculation, sludge recycle, SRT(solids residence time), influent COD/TN and HRT(hydraulic retention time) were studied. Results indicated that it was possible to increase nitrogen removal by using corresponding control strategies, such as, adjusting the DO set point according to effluent ammonia concentration; manipulating nitrate recirculation flow according to nitrate concentration at the end of anoxic zone. Based on the experiments results, a knowledge-based approach for supervision of the nitrogen removal problems was considered, and decision trees for diagnosing nitrification and denitrification problems were built and successfully applied to A/O process.

关键词： A/O process nitrification denitrification nitrogen removal decision trees

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Selecting decision trees for power system security assessment

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能源与人工智能(英文)

能源与人工智能(英文) 2021年第4期6卷 21-30页

作者： Al-Amin B.Bugaje Jochen L.Cremer Mingyang Sun Goran Strbac Department of Electrical&Electronic Engineering Imperial College LondonLondonSW72AZUK Department of Electrical Sustainable Energy TU DelftMekelweg 52628 CD DelftNetherlands Department of Control Science and Engineering Zhejiang UniversityHangzhou 310027China

Power systems transport an increasing amount of electricity,and in the future,involve more distributed renewables and dynamic interactions of the equipment.The system response to disturbances must be secure and predictable to avoid power blackouts.The system response can be simulated in the time domain.However,this dynamic security assessment(DSA)is not computationally tractable in real-time.Particularly promising is to train decision trees(DTs)from machine learning as interpretable classifiers to predict whether the systemwide responses to disturbances are secure.In most research,selecting the best DT model focuses on predictive accuracy.However,it is insufficient to focus solely on predictive accuracy.Missed alarms and false alarms have drastically different costs,and as security assessment is a critical task,interpretability is crucial for operators.In this work,the multiple objectives of interpretability,varying costs,and accuracies are considered for DT model selection.We propose a rigorous workflow to select the best classifier.In addition,we present two graphical approaches for visual inspection to illustrate the selection sensitivity to probability and impacts of disturbances.We propose cost curves to inspect selection combining all three objectives for the first time.Case studies on the IEEE 68 bus system and the French system show that the proposed approach allows for better DT-selections,with an 80%increase in interpretability,5%reduction in expected operating cost,while making almost zero accuracy compromises.The proposed approach scales well with larger systems and can be used for models beyond DTs.Hence,this work provides insights into criteria for model selection in a promising application for methods from artificial intelligence(AI).

关键词： Dynamic security assessment Machine learning decision trees ROC curve Cost curves Cost sensitivity

Analysis of NIR spectroscopic data using decision trees and their ensembles

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Journal of Analysis and Testing 2018年第3期2卷 274-289页

作者： Sergey Kucheryavskiy Department of Chemistry and Bioscience Aalborg UniversityNiels Bohrs vej8EsbjergDenmark

decision trees and their ensembles became quite popular for data analysis during the past decade.One of the main reasons for that is current boom in big data,where traditional statistical methods(such as,e.g.,multiple linear regression)are not very efficient.However,in chemometrics these methods are still not very widespread,first of all because of several limitations related to the ratio between number of variables and observations.This paper presents several examples on how decision trees and their ensembles can be used in analysis of NIR spectroscopic data both for regression and classification.We will try to consider all important aspects including optimization and validation of models,evaluation of results,treating missing data and selection of most important variables.The performance and outcome of the decision tree-based methods are compared with more traditional approach based on partial least squares.

关键词： NIR spectroscopy decision trees Classification and regression trees Random forests

A Preliminary Study of Interpreting CNNs Using Soft decision trees

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国际计算机前沿大会会议论文集 2022年第1期 152-162页

作者： Qilong Zhao Yue Wang School of Information Central University of Finance and EconomicsBeijingChina

decision trees can be used to enhance the interpretability of neural networks.In this work,we compare the classification and interpretability performance of the normal decision tree and a type of soft decision tree when they are used to interpret the decision paths of CNN networks.With the help of feature visualization and human-labeled features,we demonstrate that the soft decision trees identify more consistent features while maintaining much higher classification performance than the normal decision tree.

关键词： CNNs Interpretability decision trees

A Dimensional Reduction Approach Based on Essential Constraints in Linear Programming

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American Journal of Operations Research 2024年第1期14卷 1-31页

作者： Eirini I. Nikolopoulou George S. Androulakis Department of Mathematics University of Patras Patras Greece Department of Business Administration University of Patras Patras Greece

This paper presents a new dimension reduction strategy for medium and large-scale linear programming problems. The proposed method uses a subset of the original constraints and combines two algorithms: the weighted average and the cosine simplex algorithm. The first approach identifies binding constraints by using the weighted average of each constraint, whereas the second algorithm is based on the cosine similarity between the vector of the objective function and the constraints. These two approaches are complementary, and when used together, they locate the essential subset of initial constraints required for solving medium and large-scale linear programming problems. After reducing the dimension of the linear programming problem using the subset of the essential constraints, the solution method can be chosen from any suitable method for linear programming. The proposed approach was applied to a set of well-known benchmarks as well as more than 2000 random medium and large-scale linear programming problems. The results are promising, indicating that the new approach contributes to the reduction of both the size of the problems and the total number of iterations required. A tree-based classification model also confirmed the need for combining the two approaches. A detailed numerical example, the general numerical results, and the statistical analysis for the decision tree procedure are presented.

关键词： Linear Programming Binding Constraints Dimension Reduction Cosine Similarity decision Analysis decision trees

Fuzzy ID3 decision trees for Data Classification:Application in Meteorology and Atmospheric Sciences

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Fuzzy ID3 Decision Trees for Data Classification:Application...

One-against-all-based Hellinger distance decision tree for multiclass imbalanced learning

2010 The 3rd International Conference on Computational Intelligence and Industrial Application (PACIIA2010)

作者： Chih-Chiang Wei Department of Information Management Toko University

The forecast of hourly precipitations in typhoon seasons has received much attention in recent years. It is important in meteorology and atmospheric sciences. This paper presents a novel algorithm,Fuzzy ID3 decision trees （FIDs）,for the precipitations during typhoon periods for a reservoir watershed. The FIDs algorithm was constructed as the quantitative precipitation forecast （QPF） model. This study also constructed the traditional average statistical model （AVS） to compare with the performance by FIDs model. The steps of flowchart involve collecting typhoon data,preprocessing the typhoon patterns,building QPF models,and training and testing the models. The developed methodology was applied to the Shihmen Reservoir watershed in Tahan River Basin. The results obtained from both FIDs and AVS models including the analysis of the 1-,3-,and 6-hr accumulated rainfalls during typhoon periods. The results showed that the superior relative mean square error （RMSE） performance by using FIDs in contrast to those by using traditional AVS. Consequently,the FIDs model demonstrated its feasibility.

关键词： decision trees Fuzzy Typhoon Prediction.

来源： cnki会议评论

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Frontiers of Information Technology & Electronic Engineering 2022年第2期23卷 278-290页

作者： Minggang DONG Ming LIU Chao JING School of Information Science and Engineering Guilin University of TechnologyGuilin 541004China Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin 541004China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic TechnologyGuilin 541004China

Since traditional machine learning methods are sensitive to skewed distribution and do not consider the characteristics in multiclass imbalance problems,the skewed distribution of multiclass data poses a major challenge to machine learning algorithms.To tackle such issues,we propose a new splitting criterion of the decision tree based on the one-against-all-based Hellinger distance(OAHD).Two crucial elements are included in OAHD.First,the one-against-all scheme is integrated into the process of computing the Hellinger distance in OAHD,thereby extending the Hellinger distance decision tree to cope with the multiclass imbalance problem.Second,for the multiclass imbalance problem,the distribution and the number of distinct classes are taken into account,and a modified Gini index is designed.Moreover,we give theoretical proofs for the properties of OAHD,including skew insensitivity and the ability to seek a purer node in the decision tree.Finally,we collect 20 public real-world imbalanced data sets from the Knowledge Extraction based on Evolutionary Learning(KEEL)repository and the University of California,Irvine(UCI)repository.Experimental and statistical results show that OAHD significantly improves the performance compared with the five other well-known decision trees in terms of Precision,F-measure,and multiclass area under the receiver operating characteristic curve(MAUC).Moreover,through statistical analysis,the Friedman and Nemenyi tests are used to prove the advantage of OAHD over the five other decision trees.

关键词： decision trees Multiclass imbalanced learning Node splitting criterion Hellinger distance One-against-all scheme

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Application of four machine-learning methods to predict short-horizon wind energy

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Global Energy Interconnection 2023年第6期6卷 726-737页

作者： Doha Bouabdallaoui Touria Haidi Faissal Elmariami Mounir Derri El Mehdi Mellouli Laboratory LAGES Ecole Hassania des Travaux Publics(EHTP)Casablanca 20230Morocco Laboratory LESE Ecole Nationale Supérieure d’Electricitéet de Mécanique(ENSEM)Casablanca 20100Morocco Laboratory LESA School of Applied SciencesSidi Mohamed Ben Abdellah UniversityFez 30000Morocco

Renewable energy has garnered attention due to the need for sustainable energy sources.Wind power has emerged as an alternative that has contributed to the transition towards cleaner energy.As the importance of wind energy grows,it can be crucial to provide forecasts that optimize its performance potential.Artificial intelligence(AI)methods have risen in prominence due to how well they can handle complicated systems while enhancing the accuracy of prediction.This study explored the area of AI to predict wind-energy production at a wind farm in Yalova,Turkey,using four different AI approaches:support vector machines(SVMs),decision trees,adaptive neuro-fuzzy inference systems(ANFIS)and artificial neural networks(ANNs).Wind speed and direction were considered as essential input parameters,with wind energy as the target parameter,and models are thoroughly evaluated using metrics such as the mean absolute percentage error(MAPE),coefficient of determination(R~2),and mean absolute error(MAE).The findings accentuate the superior performance of the SVM,which delivered the lowest MAPE(2.42%),the highest R~2(0.95),and the lowest MAE(71.21%)compared with actual values,while ANFIS was less effective in this context.The main aim of this comparative analysis was to rank the models to move to the next step in improving the least efficient methods by combining them with optimization algorithms,such as metaheuristic algorithms.

关键词： Wind Energy Prediction Support Vector Machines decision trees Adaptive Neuro-Fuzzy Inference Systems Artificial Neural Networks

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