Random forest can accurately predict the development of end-stage renal disease in immunoglobulin a nephropathy patients

Han, Xin; Zheng, Xiaonan; Wang, Ying; Sun, Xiaoru; Xiao, Yi; Tang, Yi; Qin, Wei

doi:10.21037/atm.2018.12.11

Cited by 34 publications

(23 citation statements)

References 24 publications

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“…Chen et al designated a subset of 10 features from the initial total number of 36 variables [ 16 ]. Compared to the work of Han et al we extended the functionality of the program with the possibility of assessing the rate of decline in renal function, expressed as a change in creatinine concentration [ 17 ].…”

Section: Discussionmentioning

confidence: 99%

Machine Learning in Prediction of IgA Nephropathy Outcome: A Comparative Approach

Konieczny

Stojanowski

Krajewska

et al. 2021

JPM

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We are overwhelmed by a deluge of data and, although its interpretation is challenging, fortunately, information technology comes to the rescue. One of the tools is artificial intelligence, allowing the identification of relationships between variables and their arbitrary classification. We focused on the assessment of both the remission of proteinuria and the deterioration of kidney function in patients with IgA nephropathy, comparing several methods of machine learning. It is of utmost importance to respond to subtle changes in kidney function, which will lead to a deceleration of the disease. This goal has been achieved by analyzing regression techniques, predicting the difference in serum creatinine concentration. We obtained the performance of the tested models which classified patients with high accuracy (Random Forest Classifier showed an accuracy of 0.8–1.0, Multi-Layer Perceptron an Area Under Curve of 0.8842–0.9035 and an accuracy of 0.7527–1.0) and regressors with a low estimation error (Decision Tree Regressor showed MAE 0.2059, RMSE 0.2645). We have demonstrated the impact of both model selection and input features on performance. Application of machine learning methods requires careful selection of models and assessed parameters. The computing power of modern computers allows searching for the models most effective in terms of accuracy.

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Section: Discussionmentioning

confidence: 99%

Machine Learning in Prediction of IgA Nephropathy Outcome: A Comparative Approach

Konieczny

Stojanowski

Krajewska

et al. 2021

JPM

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“…Thus, it was employed to identify the most predictive variables contributing to outcomes. As an ensemble machine learning model, the RF model makes prediction by combining the outputs of a multitude of base models, thereby reducing the bias that may occur in single learning models (29). Various LOS prediction tools have been developed for other diseases.…”

Section: Discussionmentioning

confidence: 99%

“…In the machine learning area, ensemble learning models usually perform better than single learning models (29,30). In the present study, both models were employed to develop LOS prediction tools and identify the predictive factors with the most contribution to the outcome of pLOS.…”

Section: Machine Learning Methodsmentioning

confidence: 98%

Development of a scoring tool for predicting prolonged length of hospital stay in peritoneal dialysis patients through data mining

Kong

Lin

et al. 2020

Ann Transl Med

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Background: The hospital admission rate is high in patients treated with peritoneal dialysis (PD), and the length of stay (LOS) in the hospital is a key indicator of medical resource allocation. This study aimed to develop a scoring tool for predicting prolonged LOS (pLOS) in PD patients by combining machine learning and traditional logistic regression (LR).Methods: This study was based on patient data collected using the Hospital Quality Monitoring System (HQMS) in China. Three machine learning methods, classification and regression tree (CART), random forest (RF), and gradient boosting decision tree (GBDT), were used to develop models to predict pLOS, which is longer than the average LOS, in PD patients. The model with the best prediction performance was used to identify predictive factors contributing to the outcome. A multivariate LR model based on the identified predictors was then built to derive the score assigned to each predictor. Finally, a scoring tool was developed, and it was tested by stratifying PD patients into different pLOS risk groups.Results: A total of 22,859 PD patients were included in our study, with 25.2% having pLOS. Among the three machine learning models, the RF model achieved the best prediction performance and thus was used to identify the 10 most predictive variables for building the scoring system. The multivariate LR model based on the identified predictors showed good discrimination power with an AUROC of 0.721 in the test dataset, and its coefficients were used as a basis for scoring tool development. On the basis of the developed scoring tool, PD patients were divided into three groups: low risk (≤5), median risk [5][6][7][8][9][10], and high risk (>10).The observed pLOS proportions in the low-risk, median-risk, and high-risk groups in the test dataset were 11.4%, 29.5%, and 54.7%, respectively.Conclusions: This study developed a scoring tool to predict pLOS in PD patients. The scoring tool can effectively discriminate patients with different pLOS risks and be easily implemented in clinical practice. The pLOS scoring tool has a great potential to help physicians allocate medical resources optimally and achieve improved clinical outcomes.

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“… Zhou et al (2017) used the RF classifier to select feature genes from mRNA microarray data to diagnose renal fibrosis. Han et al (2019) used RF to predict the developments of end-stage renal diseases in immunoglobulin nephropathy patients. SVM is a novel machine learning method that simplifies the usual classification and regression problems.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Models to Predict Primary Sites of Metastatic Cervical Carcinoma From Unknown Primary

Jiang

Liu

et al. 2020

Front. Genet.

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Metastatic cervical carcinoma from unknown primary (MCCUP) accounts for 1–4% of all head and neck tumors, and identifying the primary site in MCCUP is challenging. The most common histopathological type of MCCUP is squamous cell carcinoma (SCC), and it remains difficult to identify the primary site pathologically. Therefore, it seems necessary and urgent to develop novel and effective methods to determine the primary site in MCCUP. In the present study, the RNA sequencing data of four types of SCC and Pan-Cancer from the cancer genome atlas (TCGA) were obtained. And after data pre-processing, their differentially expressed genes (DEGs) were identified, respectively. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis indicated that these significantly changed genes of four types of SCC share lots of similar molecular functions and histological features. Then three machine learning models, [Random Forest (RF), support vector machine (SVM), and neural network (NN)] which consisted of ten genes to distinguish these four types of SCC were developed. Among the three models with prediction tests, the RF model worked best in the external validation set, with an overall predictive accuracy of 88.2%, sensitivity of 88.71%, and specificity of 95.42%. The NN model is the second in efficacy, with an overall accuracy of 82.02%, sensitivity of 81.23%, and specificity of 93.04%. The SVM model is the last, with an overall accuracy of 76.69%, sensitivity of 74.81%, and specificity of 90.84%. The present analysis of similarities and differences among the four types of SCC, and novel models developments for distinguishing four types of SCC with informatics methods shed lights on precision MCCUP diagnosis in the future.

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Random forest can accurately predict the development of end-stage renal disease in immunoglobulin a nephropathy patients

Cited by 34 publications

References 24 publications

Machine Learning in Prediction of IgA Nephropathy Outcome: A Comparative Approach

Machine Learning in Prediction of IgA Nephropathy Outcome: A Comparative Approach

Development of a scoring tool for predicting prolonged length of hospital stay in peritoneal dialysis patients through data mining

Machine Learning Models to Predict Primary Sites of Metastatic Cervical Carcinoma From Unknown Primary

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