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Development of a machine learning-based model to predict prognosis of alpha-fetoprotein-positive hepatocellular carcinoma

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JOURNAL OF TRANSLATIONAL MEDICINE 2024年第1期22卷 455页

作者： Dong, Bingtian Zhang, Hua Duan, Yayang Yao, Senbang Chen, Yongjian Zhang, Chaoxue Anhui Med Univ Affiliated Hosp 1 Dept Ultrasound Hefei Peoples R China Westlake Univ Affiliated Hangzhou Peoples Hosp 1 Sch Med Dept Ultrasound Hangzhou Peoples R China Anhui Med Univ Affiliated Hosp 2 Dept Oncol Hefei Anhui Peoples R China Anhui Med Univ Dept Oncol Hefei Anhui Peoples R China Fujian Med Univ Affiliated Hosp 2 Dept Ultrasound Quanzhou Peoples R China

Background Patients with alpha-fetoprotein (AFP)-positive hepatocellular carcinoma (HCC) have aggressive biological behavior and poor prognosis. Therefore, survival time is one of the greatest concerns for patients with AFP-positive HCC. This study aimed to demonstrate the utilization of six machine learning (ML)-based prognostic models to predict overall survival of patients with AFP-positive *** Data on patients with AFP-positive HCC were extracted from the Surveillance, Epidemiology, and End Results database. Six ML algorithms (extreme gradient boosting [xgboost], logistic regression [LR], support vector machine [SVM], random forest [RF], K-nearest neighbor [KNN], and decision tree [ID3]) were used to develop the prognostic models of patients with AFP-positive HCC at one year, three years, and five years. Area under the receiver operating characteristic curve (AUC), confusion matrix, calibration curves, and decision curve analysis (DCA) were used to evaluate the *** A total of 2,038 patients with AFP-positive HCC were included for analysis. The 1-, 3-, and 5-year overall survival rates were 60.7%, 28.9%, and 14.3%, respectively. Seventeen features regarding demographics and clinicopathology were included in six ML algorithms to generate a prognostic model. The xgboost model showed the best performance in predicting survival at 1-year (train set: AUC = 0.771;test set: AUC = 0.782), 3-year (train set: AUC = 0.763;test set: AUC = 0.749) and 5-year (train set: AUC = 0.807;test set: AUC = 0.740). Furthermore, for 1-, 3-, and 5-year survival prediction, the accuracy in the training and test sets was 0.709 and 0.726, 0.721 and 0.726, and 0.778 and 0.784 for the xgboost model, respectively. Calibration curves and DCA exhibited good predictive performance as *** The xgboost model exhibited good predictive performance, which may provide physicians with an effective tool for early medical intervention and improve the survival of patients.

关键词： Hepatocellular carcinoma Predictive analytics Survival Machine learning xgboost algorithm

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Mass flow characteristics prediction of refrigerants through electronic expansion valve based on xgboost

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INTERNATIONAL JOURNAL OF REFRIGERATION 2024年 158卷 345-352页

作者： Wan, Anping Gong, Zhipeng Chen, Ting AL-Bukhaiti, Khalil Hangzhou City Univ Dept Mechatron Engn Hangzhou 310015 Peoples R China Zhejiang Univ Technol Dept Mech Engn Hangzhou 310014 Peoples R China Southwest Jiaotong Univ Sch Civil Engn Chengdu 610031 Peoples R China

In an effort to enhance the prediction accuracy of the mass flow characteristics of refrigerants through electronic expansion valves (EEVs), this study develops a mass flow model using the xgboost machine learning algorithm. Utilizing experimental data from open literature for refrigerants R1233zd(E) and R245fa, the model aims to accurately predict the mass flow coefficient in EEVs, a crucial aspect for improving the performance and efficiency of refrigeration systems. The model's performance is evaluated using the coefficient of determination (R2) on the training and test datasets, revealing a minimal performance gap and no overfitting issue. Remarkably, our model's predictions for R1233zd(E) and R245fa datasets align consistently with experimental data, with 96.4 % and 90.6 % of the predicted data deviating from the experimental ones within +/- 5 %, respectively. Further, the root-mean-square error (RMSE) and coefficient of variation of the root mean square error (CV-RMSE) values are quite low, with 2.2 % and 2.33 % for R1233zd(E), and 4.23 % and 4.47 % for R245fa, indicating high prediction accuracy. Compared to the original models, the paper approach with the xgboost algorithm significantly improves prediction accuracy. This advancement provides a promising direction for developing more efficient and reliable control strategies for refrigeration systems in various applications.

关键词： Electronic expansion valve Mass flow rate xgboost algorithm Prediction accuracy Refrigeration systems Motscles Detendeur electronique Debit massique algorithme xgboost Precision de la prediction Systemes frigorifiques

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A comparative assessment of tree-based predictive models to estimate geopolymer concrete compressive strength

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NEURAL COMPUTING & APPLICATIONS 2023年第9期35卷 6569-6588页

作者： May Huu Nguyen Hai-Van Thi Mai Son Hoang Trinh Hai-Bang Ly Univ Transport Technol Hanoi 100000 Vietnam Hiroshima Univ Grad Sch Adv Sci & Engn Civil & Environm Engn Program 1-4-1 Kagamiyama Higashihiroshima Hiroshima 7398527 Japan

Fly ash-based geopolymer concrete (FA-GPC) is a material that might be utilized to build a more sustainable construction industry;therefore, this paper aims to develop a novel approach for its compressive strength (CS) prediction. To achieve this goal, three tree-based machine learning methods, namely, Radom Forest (RF), Decision Tree (DT), and Extreme Gradient Boosting (xgboost), were developed based on experimental databases considering fourteen key factors of FA-GPC. The results indicated that xgboost outperformed other methods, as proved by excellent prediction metrics. The sensitivity analysis of xgboost models was then discussed utilizing feature importance, mean Shapley additive explanations (SHAP), and Beeswarm-SHAP values. The findings indicated that FA content had the most crucial impact on the CS, followed by SiO2 and NaOH contents, among the other variables examined. Finally, the SHAP dependence plots technique was utilized to quantitatively discuss feature interactions and contributions to the CS of FA-GPC.

关键词： xgboost algorithm Fly ash-based geopolymer concrete Compressive strength Machine learning

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Prediction Dynamics in Cotton Aphid Using Unmanned Aerial Vehicle Multispectral Images and Vegetation Indices

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IEEE ACCESS 2023年 11卷 5908-5918页

作者： Jiang, Pingan Zhou, Xuelin Liu, Tonglai Guo, Xiaohu Ma, Deying Zhang, Cong Li, Yan Liu, Shuangyin Xinjiang Agr Univ Coll Grassland Sci Urumqi 830052 Peoples R China Zhongkai Univ Agr & Engn Coll Informat Sci & Technol Guangzhou 510225 Peoples R China Xinjiang Agr Univ Coll Agr Urumqi 830052 Peoples R China Xinjiang Agr Univ Coll Resource & Environm Urumqi 830052 Peoples R China Kunming Univ Sci & Technol Fac Land & Resources Engn Kunming 650221 Peoples R China Zhuhai Orbita Aerosp Sci & Technol Co Ltd Zhuhai 519080 Peoples R China

Cotton harvest can be increased by having real-time information on the state of cotton aphid populations. However, traditional cotton aphid monitoring relies on ground sample methods supported by models such as linear regression, resulting in low forecast accuracy. Therefore, this paper purposes to enhance the precision of the remote sensing prediction model by investigating the cotton aphid prediction model construction approach. We explored the effectiveness of the xgboost algorithm combined with the GWO algorithm and SVR method for cotton aphid prediction relying on vegetation indices derived from UAV multispectral photography. Originally, 12 indices related to cotton aphids were calculated by UAV multispectral reflectance. Additionally, the optimal index combination for pest prediction was determined utilizing analysis of correction and two-way ANOVA, combined with the xgboost algorithm. Furthermore, a pest prevalence prediction model for cotton aphids was constructed via the SVR methodology associated with the optimal catalog combination, and the model was optimized using the GWO algorithm. Compared with the seven algorithms, experimental results demonstrate that the MSE and MAE of the xgboostGWO-SVR model are reduced by 90.20% and 70.36% (SVR), 90.14% and 70.26% (xgboost-SVR), 7.47% and 0.14% (xgboost-GA-SVR), 5.80% and 0.11% (xgboost-PSO-SVR), 12.06% and 58.95% (LR), and 84.77% and 89.22% (BPNN), whereas the R-2 is increased by 22.5% (SVR and xgboost-SVR), 0.3% (LR), and 12.51% (BPNN). The R-2 of the prediction model of xgboost-SVR combined with GWO, PSO, and GA is not significantly different. Among these models, the xgboost-GWO-SVR obtained the highest R-2 of 0.980 and the lowest MAE of 2.838.

关键词： Cotton Predictive models Prediction algorithms Autonomous aerial vehicles Vegetation mapping Vehicle dynamics Statistics Social factors Remote sensing Reflectivity Crops Agriculture Cotton aphid support vector regression unmanned aerial vehicle xgboost algorithm integrated pest management grey wolf optimizer

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Optimization and prediction in the early design stage of office buildings using genetic and xgboost algorithms

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BUILDING AND ENVIRONMENT 2022年 218卷

作者： Yan, Hainan Yan, Ke Ji, Guohua Nanjing Univ Sch Architecture & Urban Planning Nanjing 210093 Peoples R China Natl Univ Singapore Dept Built Environm Singapore 117566 Singapore Nanjing Univ Sch Architecture & Urban Planning Jianliang BldgGulou Campus Nanjing 210093 Peoples R China

Incorporating intelligent optimization algorithms in the early stages of office building design facilitates a better response to the local climate. The indoor and outdoor thermal performances of office buildings, such as solar radiation, indoor lighting, and outdoor thermal comfort, must be jointly evaluated during the conceptual design phase. Based on the technical framework of "performance-based generative architectural design", this study constructs a data-driven workflow for comprehensive performance assessment and rapid prediction of office buildings. The method was then applied to an office building in the hot summer and cold winter regions of China. Based on a total of 6000 data samples generated by the iterative process of genetic optimization, this study achieved a precision of 0.77, recall of 0.59, and F-1 score of 0.75 for categorical prediction by the xgboost algorithm. The method facilitates the optimization potential of integrated solar and thermal performances in the early design phase of office buildings while significantly improving the efficiency of interaction and feedback between design decisions and their performance evaluation.

关键词： Office buildings Early design stage Building performance xgboost algorithm

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An xgboost Machine Learning Based Model for Predicting Ki-67 Value ≥ 15% in T2NXM0 Stage Primary Breast Cancer Receiving Neoadjuvant Chemotherapy Using Clinical Data and Delta-Radiomic Features on Ultrasound Images and Overall Survival Analysis: A 5-Year Postoperative Follow-Up Study

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TECHNOLOGY IN CANCER RESEARCH & TREATMENT 2024年 23卷 15330338241265989-15330338241265989页

作者： Lu, Yang Yang, Fei Tao, Yichao An, Pang Cent Hosp Xiaogan Dept Ultrasound Xiaogan 432000 Hubei Peoples R China Hubei Univ Med Xiangyang Peoples Hosp 1 Dept Radiol Xiangyang Peoples R China

Objective: To establish a model based on clinical and delta-radiomic features within ultrasound images using xgboost machine learning to predict proliferation-associated nuclear antigen Ki-67 value >= 15% in T2NXM0 stage primary breast cancer (BC). Method: Data were collected from 228 randomly selected BC patients who received ultrasound screening and postoperative pathologic assessment from April 2015 to September 2018. The patients were classified into the study group (n = 80) and control group (n = 148), and the data were apportioned into the training set and test set at a 7:3 ratio based on time intervals. In the training set, crucial factors were identified from clinical features and grayscale and delta-radiomic features within ultrasound images, by using the chi-square test, t-test, and rank-sum test. The clinical model, imaging model, and combined model were built using multivariate logistic regression, respectively. The model's predictive performance and clinical net benefit were assessed using DeLong's method and decision curve analysis. Meanwhile, an xgboost algorithm is used to establish a prediction model to verify the above results. Results: The crucial factors affecting Ki-67 value >= 15% included BMI, lymph node metastases, BC volume, CA153, pathology type, tumor boundaries, tumor morphology, elastography score, and delta-radscore. The predictive performance of the combined model [AUC 0.857, OR 0.0290, 95% CI 0.793-0.908] was considerably improved on the training set than the clinical model [AUC 0.724, OR 0.0422, 95% CI 0.648-0.792] and the imaging model [AUC 0.798, OR 0.0355, 95% CI 0.727-0.857]. The decision curve analysis also confirmed that the combined model delivered a higher clinical net benefit, and the verification on the test set yielded similar results. The nomogram and the calibration curve plotted based on the combined model achieved satisfactory clinical effects. The SHAP value of the xgboost algorithm also confirmed that lymph node meta

关键词： ultrasound examination delta-radiomics breast cancer tumor proliferating cell nuclear antigen 67 xgboost algorithm

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Predicting early gastric cancer risk using machine learning: A population-based retrospective study

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DIGITAL HEALTH 2024年 10卷

作者： Ke, Xing Cai, Xinyu Bian, Bingxian Shen, Yuanheng Zhou, Yunlan Liu, Wei Wang, Xu Shen, Lisong Yang, Junyao Shanghai Jiao Tong Univ Xinhua Hosp Dept Clin Lab Sch Med Shanghai Peoples R China Shanghai Jiao Tong Univ Coll Hlth Sci & Technol Fac Med Lab Sci Sch Med Shanghai Peoples R China Shanghai Acad Expt Med Inst Artificial Intelligence Med Shanghai Peoples R China Shanghai Jiao Tong Univ Ruijin Hosp Coll Basic Med Sci Dept PatholSch Med Shanghai Peoples R China Beijing Deepwise & League PHD Technol Co Ltd Dept Res Collaborat R&D Ctr Beijing Peoples R China Shanghai Jiao Tong Univ Key Lab Cell Differentiat & Apoptosis Chinese Minist Educ Sch Med Shanghai Peoples R China Shanghai Jiao Tong Univ Xinhua Hosp Dept Clin Lab Sch Med 1665 Kong Jiang Rd Shanghai 200092 Peoples R China Shanghai Jiao Tong Univ Ruijin Hosp Dept Pathol Sch Med 280 Chongqing South Ave Shanghai 200025 Peoples R China Shanghai Jiao Tong Univ Coll Basic Med Sci Sch Med 280 Chongqing South Ave Shanghai 200025 Peoples R China

Background Early detection and treatment are crucial for reducing gastrointestinal tumour-related mortality. The diagnostic efficiency of the most commonly used diagnostic markers for gastric cancer (GC) is not very high. A single laboratory test cannot meet the requirements of early screening, and machine learning methods are needed to aid the early diagnosis of GC by combining multiple *** Based on the xgboost algorithm, a new model was developed to distinguish between GC and precancerous lesions in newly admitted patients between 2018 and 2023 using multiple laboratory tests. We evaluated the ability of the prediction score derived from this model to predict early GC. In addition, we investigated the efficacy of the model in correctly screening for GC given negative protein tumour marker *** The XHGC20 model constructed using the xgboost algorithm could distinguish GC from precancerous disease well (area under the receiver operating characteristic curve [AUC] = 0.901), with a sensitivity, specificity and cut-off value of 0.830, 0.806 and 0.265, respectively. The prediction score was very effective in the diagnosis of early GC. When the cut-off value was 0.27, and the AUC was 0.888, the sensitivity and specificity were 0.797 and 0.807, respectively. The model was also effective at evaluating GC given negative conventional markers (AUC = 0.970), with the sensitivity and specificity of 0.941 and 0.906, respectively, which helped to reduce the rate of missed *** The XHGC20 model established by the xgboost algorithm integrates information from 20 clinical laboratory tests and can aid in the early screening of GC, providing a useful new method for auxiliary laboratory diagnosis.

关键词： Gastric cancer diagnostic efficacy machine learning xgboost algorithm early diagnosis

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Data-Driven Traffic Accident Analysis and Prediction Using Machine Learning Models: A Case Study of Philadelphia City 7

Data-Driven Traffic Accident Analysis and Prediction Using M...

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7th International Conference on Traffic Engineering and Transportation System (ICTETS)

作者： Lyu, Chengxuan Penn State Univ Sch Elect Engn & Comp Sci 201 Old Main University Pk PA 16802 USA

ISBN: (纸本)9781510674479

Traffic safety problem has been highly concerned by people all over the world. Predicting potential traffic accidents can help to improve traffic facilities or emergency system, and give people alerts to potential dangers. As a random event, the occurrence of traffic accidents has obvious seasonal and spatial characteristics. The accuracy of traffic accident prediction can be improved by combining the seasonal and spatial characteristics of traffic accidents. In the study, we selected Philadelphia car accident databases during 2008 to 2012 on Opendata Philly. On the basis of data grouping preprocessing, we tested three machine learning algorithms to predict the count of accidents. The results indicate that LSTM(Long Short-Term Memory) algorithm has the best performance relatively, and xgboost (eXtreme Gradient Boosting) model does not perform better than ARIMA(Autoregressive Integrated Moving Average model. In addition, we identify the important features of traffic accidents in Philadelphia city: the count of accidents on the same road segment, traffic control device, automobile type, roadway surface condition, weather condition. The datasets in this study has obvious spatial characteristics also, and the occurrence of accidents is closely related to the accident segment.

关键词： Traffic accidents Data analysis Machine learning ARIMA algorithm xgboost algorithm LSTM algorithm

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Identifying Leukoaraiosis with Mild Cognitive Impairment by Fusing Multiple MRI Morphological Metrics and Ensemble Machine Learning

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JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024年第2期37卷 666-678页

作者： Yang, Yifeng Hu, Ying Chen, Yang Gu, Weidong Nie, Shengdong Univ Shanghai Sci & Technol Sch Hlth Sci & Engn Shanghai 200093 Peoples R China Shanghai Jiao Tong Univ Renji Hosp Sch Med Dept Radiol Shanghai 200127 Peoples R China Fudan Univ Huadong Hosp Dept Anesthesiol Shanghai 200040 Peoples R China

Leukoaraiosis (LA) is strongly associated with impaired cognition and increased dementia risk. Determining effective and robust methods of identifying LA patients with mild cognitive impairment (LA-MCI) is important for clinical intervention and disease monitoring. In this study, an ensemble learning method that combines multiple magnetic resonance imaging (MRI) morphological features is proposed to distinguish LA-MCI patients from LA patients lacking cognitive impairment (LA-nCI). Multiple comprehensive morphological measures (including gray matter volume (GMV), cortical thickness (CT), surface area (SA), cortical volume (CV), sulcus depth (SD), fractal dimension (FD), and gyrification index (GI)) are extracted from MRI to enrich model training on disease characterization information. Then, based on the general extreme gradient boosting (xgboost) classifier, we leverage a weighted soft-voting ensemble framework to ensemble a data-level resampling method (Fusion + xgboost) and an algorithm-level focal loss (FL)-improved xgboost model (FL-xgboost) to overcome class-imbalance learning problems and provide superior classification performance and stability. The baseline xgboost model trained on an original imbalanced dataset had a balanced accuracy (Bacc) of 78.20%. The separate Fusion + xgboost and FL-xgboost models achieved Bacc scores of 80.53 and 81.25%, respectively, which are clear improvements (i.e., 2.33% and 3.05%, respectively). The fused model distinguishes LA-MCI from LA-nCI with an overall accuracy of 84.82%. Sensitivity and specificity were also well improved (85.50 and 84.14%, respectively). This improved model has the potential to facilitate the clinical diagnosis of LA-MCI.

关键词： White matter hyperintensities MRI morphometry xgboost algorithm Imbalance learning Ensemble learning

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Prediction of the jump height of transmission lines after ice-shedding based on xgboost and Bayesian optimization

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COLD REGIONS SCIENCE AND TECHNOLOGY 2023年第1期213卷

作者： Long, Xiaohong Gu, Xiaopeng Lu, Chunde Li, Zonglin Ma, Yongtao Jian, Zhou Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Hubei Key Lab Control Struct Wuhan 430074 Peoples R China State Grid Hunan Elect Power Co Ltd Disaster Prevent & Reduct Ctr Changsha 410129 Peoples R China

The maximum jump height after ice-shedding must be determined to avoid jumps in transmission lines caused by ice-shedding, which can result in interphase flashovers and collisions that endanger the safety of transmission engineering. In this work, a numerical study of transmission lines under different structural, ice and wind parameters is carried out to create a dataset with a total of 1980 data for 11 input features that have a significant effect on the jump height. A data-driven model BO-xgboost combined with Bayesian optimisation (BO) and Extreme Gradient Boosting (xgboost) algorithm is proposed to predict the jump height of transmission lines after ice-shedding. Analysis results indicate that the application of the BO algorithm to the hyperparameter optimisation of the xgboost model can improve the prediction accuracy whilst maintaining high efficiency. Meanwhile, the proposed BO-xgboost model is superior to other benchmark models in various performance indicators, and strong correlations are discovered between the predicted and the target values. In addition, the proposed model has the advantages of high reliability and interpretability and can rapidly and accurately predict the maximum jump height of a transmission line after ice-shedding, which provides an effective and convenient means for the electrical insulation clearance design of transmission lines.

关键词： Prediction model Ice -shedding Jump height xgboost algorithm Bayesian optimization

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