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Niu Q, Wang G, Liu B, Zhang R, Lei J, Wang H, Liu M. Selection and prediction of metro station sites based on spatial data and random forest: a study of Lanzhou, China. Sci Rep 2023; 13:22542. [PMID: 38110563 PMCID: PMC10728089 DOI: 10.1038/s41598-023-49877-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 12/13/2023] [Indexed: 12/20/2023] Open
Abstract
Urban economic development, congestion relief, and traffic efficiency are all greatly impacted by the thoughtful planning of urban metro station layout. with the urban area of Lanzhou as an example, the suitability of the station locations of the built metro stations of the rail transit lines 1 and 2 in the study area have been evaluated using multi-source heterogeneous spatial data through data collection, feature matrix construction, the use of random forest and K-fold cross-validation, among other methods. The average Gini reduction value was used to examine the contribution rate of each feature indicator based on the examination of model truthfulness. According to the study's findings: (1) K-fold cross-validation was applied to test the random forest model that was built using the built metro stations and particular factors. The average accuracy of the tests and out-of-bag data (OOB) of tenfold cross-validation were 89.62% and 91.285%, respectively. Additionally, the AUC area under the ROC curve was 0.9823, indicating that this time, from the perspective of the natural environment, traffic location, and social factors The 19 elements selected from the views of the urban function structure, social economics, and natural environment are closely associated to the locations of the metro station in the research region, and the prediction the findings are more reliable; (2) It becomes apparent that more than half of the built station sites display excellent agreement with the predicted sites in terms of geographical location by superimposing the built metro station sites with the prediction results and tally up their cumulative prediction probability values within the 300 m buffering zone; (3) Based on the contribution rate of each indicator to the model, transport facilities, companies, population density, night lighting, science, education and culture, residential communities, and road network density are identified as the primary influential factors, each accounting for over 6.6%. Subsequently, land use, elevation, and slope are found to have relatively lower contributions. The results of the research provided important information for the local metro's best location selection and planning.
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Affiliation(s)
- Quanfu Niu
- School of Civil Engineering, Lanzhou University of Technology, Lanzhou, 730050, China.
- Emergency Mapping Engineering Research Center of Gansu Province, Lanzhou, 730050, China.
- Academician Expert Workstation of Gansu Dayu Jiu Zhou Space Information Technology Co., Ltd, Lanzhou, 730050, China.
| | - Gang Wang
- School of Civil Engineering, Lanzhou University of Technology, Lanzhou, 730050, China
| | - Bo Liu
- School of Civil Engineering, Lanzhou University of Technology, Lanzhou, 730050, China
| | - Ruizhen Zhang
- School of Civil Engineering, Lanzhou University of Technology, Lanzhou, 730050, China
| | - Jiaojiao Lei
- School of Civil Engineering, Lanzhou University of Technology, Lanzhou, 730050, China
| | - Hao Wang
- School of Civil Engineering, Lanzhou University of Technology, Lanzhou, 730050, China
| | - Mingzhi Liu
- School of Civil Engineering, Lanzhou University of Technology, Lanzhou, 730050, China
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Tsai MH, Jhou MJ, Liu TC, Fang YW, Lu CJ. An integrated machine learning predictive scheme for longitudinal laboratory data to evaluate the factors determining renal function changes in patients with different chronic kidney disease stages. Front Med (Lausanne) 2023; 10:1155426. [PMID: 37859858 PMCID: PMC10582636 DOI: 10.3389/fmed.2023.1155426] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 09/19/2023] [Indexed: 10/21/2023] Open
Abstract
Background and objectives Chronic kidney disease (CKD) is a global health concern. This study aims to identify key factors associated with renal function changes using the proposed machine learning and important variable selection (ML&IVS) scheme on longitudinal laboratory data. The goal is to predict changes in the estimated glomerular filtration rate (eGFR) in a cohort of patients with CKD stages 3-5. Design A retrospective cohort study. Setting and participants A total of 710 outpatients who presented with stable nondialysis-dependent CKD stages 3-5 at the Shin-Kong Wu Ho-Su Memorial Hospital Medical Center from 2016 to 2021. Methods This study analyzed trimonthly laboratory data including 47 indicators. The proposed scheme used stochastic gradient boosting, multivariate adaptive regression splines, random forest, eXtreme gradient boosting, and light gradient boosting machine algorithms to evaluate the important factors for predicting the results of the fourth eGFR examination, especially in patients with CKD stage 3 and those with CKD stages 4-5, with or without diabetes mellitus (DM). Main outcome measurement Subsequent eGFR level after three consecutive laboratory data assessments. Results Our ML&IVS scheme demonstrated superior predictive capabilities and identified significant factors contributing to renal function changes in various CKD groups. The latest levels of eGFR, blood urea nitrogen (BUN), proteinuria, sodium, and systolic blood pressure as well as mean levels of eGFR, BUN, proteinuria, and triglyceride were the top 10 significantly important factors for predicting the subsequent eGFR level in patients with CKD stages 3-5. In individuals with DM, the latest levels of BUN and proteinuria, mean levels of phosphate and proteinuria, and variations in diastolic blood pressure levels emerged as important factors for predicting the decline of renal function. In individuals without DM, all phosphate patterns and latest albumin levels were found to be key factors in the advanced CKD group. Moreover, proteinuria was identified as an important factor in the CKD stage 3 group without DM and CKD stages 4-5 group with DM. Conclusion The proposed scheme highlighted factors associated with renal function changes in different CKD conditions, offering valuable insights to physicians for raising awareness about renal function changes.
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Affiliation(s)
- Ming-Hsien Tsai
- Division of Nephrology, Department of Medicine, Shin Kong Wu Ho-Su Memorial Hospital, Taipei, Taiwan
- Department of Medicine, School of Medicine, Fu Jen Catholic University, New Taipei City, Taiwan
| | - Mao-Jhen Jhou
- Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City, Taiwan
| | - Tzu-Chi Liu
- Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City, Taiwan
| | - Yu-Wei Fang
- Division of Nephrology, Department of Medicine, Shin Kong Wu Ho-Su Memorial Hospital, Taipei, Taiwan
- Department of Medicine, School of Medicine, Fu Jen Catholic University, New Taipei City, Taiwan
| | - Chi-Jie Lu
- Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City, Taiwan
- Artificial Intelligence Development Center, Fu Jen Catholic University, New Taipei City, Taiwan
- Department of Information Management, Fu Jen Catholic University, New Taipei City, Taiwan
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Liu Y, Qiu T, Hu H, Kong C, Zhang Y, Wang T, Zhou J, Zou J. Machine Learning Models for Prediction of Severe Pneumocystis carinii Pneumonia after Kidney Transplantation: A Single-Center Retrospective Study. Diagnostics (Basel) 2023; 13:2735. [PMID: 37685276 PMCID: PMC10486565 DOI: 10.3390/diagnostics13172735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Revised: 08/20/2023] [Accepted: 08/21/2023] [Indexed: 09/10/2023] Open
Abstract
BACKGROUND The objective of this study was to formulate and validate a prognostic model for postoperative severe Pneumocystis carinii pneumonia (SPCP) in kidney transplant recipients utilizing machine learning algorithms, and to compare the performance of various models. METHODS Clinical manifestations and laboratory test results upon admission were gathered as variables for 88 patients who experienced PCP following kidney transplantation. The most discriminative variables were identified, and subsequently, Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), K-Nearest Neighbor (KNN), Light Gradient Boosting Machine (LGBM), and eXtreme Gradient Boosting (XGB) models were constructed. Finally, the models' predictive capabilities were assessed through ROC curves, sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), and F1-scores. The Shapley additive explanations (SHAP) algorithm was employed to elucidate the contributions of the most effective model's variables. RESULTS Through lasso regression, five features-hemoglobin (Hb), Procalcitonin (PCT), C-reactive protein (CRP), progressive dyspnea, and Albumin (ALB)-were identified, and six machine learning models were developed using these variables after evaluating their correlation and multicollinearity. In the validation cohort, the RF model demonstrated the highest AUC (0.920 (0.810-1.000), F1-Score (0.8), accuracy (0.885), sensitivity (0.818), PPV (0.667), and NPV (0.913) among the six models, while the XGB and KNN models exhibited the highest specificity (0.909) among the six models. Notably, CRP exerted a significant influence on the models, as revealed by SHAP and feature importance rankings. CONCLUSIONS Machine learning algorithms offer a viable approach for constructing prognostic models to predict the development of severe disease following PCP in kidney transplant recipients, with potential practical applications.
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Affiliation(s)
- Yiting Liu
- Department of Organ Transplantation, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Tao Qiu
- Department of Organ Transplantation, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Haochong Hu
- Department of Organ Transplantation, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Chenyang Kong
- Department of Organ Transplantation, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Department of Nephrology, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Yalong Zhang
- Department of Organ Transplantation, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Tianyu Wang
- Department of Organ Transplantation, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Jiangqiao Zhou
- Department of Organ Transplantation, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Jilin Zou
- Department of Organ Transplantation, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
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