scholarly journals Machine Learning Approach to Predict the Probability of Recurrence after Surgery for Renal Cell Carcinoma: Prediction Model Development Study (Preprint)

10.2196/25635 ◽  
2020 ◽  
Author(s):  
HyungMin Kim ◽  
Sun Jung Lee ◽  
So Jin Park ◽  
In Young Choi ◽  
Sung-Hoo Hong
Keyword(s):  
Machine Learning ◽  
Renal Cell Carcinoma ◽  
Prediction Model ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Model Development ◽  
Learning Approach ◽  
Development Study ◽  
Machine Learning Approach

scholarly journals Coupled Mass-Spectrometry-Based Lipidomics Machine Learning Approach for Early Detection of Clear Cell Renal Cell Carcinoma

2020 ◽  
Vol 20 (1) ◽  
pp. 841-857
Author(s):  
Malena Manzi ◽  
Martín Palazzo ◽  
María Elena Knott ◽  
Pierre Beauseroy ◽  
Patricio Yankilevich ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mass Spectrometry ◽  
Renal Cell Carcinoma ◽  
Early Detection ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Learning Approach ◽  
Cell Renal Cell Carcinoma ◽  
Machine Learning Approach

Prediction Model for Prevalence of Type-2 Diabetes Mellitus Complications Using Machine Learning Approach

2019 ◽  
pp. 103-116 ◽  
Author(s):  
Muhammad Younus ◽  
Md Tahsir Ahmed Munna ◽  
Mirza Mohtashim Alam ◽  
Shaikh Muhammad Allayear ◽  
Sheikh Joly Ferdous Ara
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Prediction Model ◽  
Learning Approach ◽  
Machine Learning Approach

scholarly journals Machine learning-based prediction of acute kidney injury after nephrectomy in patients with renal cell carcinoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yeonhee Lee ◽  
Jiwon Ryu ◽  
Min Woo Kang ◽  
Kyung Ha Seo ◽  
Jayoun Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Renal Cell Carcinoma ◽  
Acute Kidney Injury ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Kidney Injury ◽  
Gradient Boosting ◽  
Scoring Model ◽  
Postoperative Aki ◽  
Better Than

AbstractThe precise prediction of acute kidney injury (AKI) after nephrectomy for renal cell carcinoma (RCC) is an important issue because of its relationship with subsequent kidney dysfunction and high mortality. Herein we addressed whether machine learning (ML) algorithms could predict postoperative AKI risk better than conventional logistic regression (LR) models. A total of 4104 RCC patients who had undergone unilateral nephrectomy from January 2003 to December 2017 were reviewed. ML models such as support vector machine, random forest, extreme gradient boosting, and light gradient boosting machine (LightGBM) were developed, and their performance based on the area under the receiver operating characteristic curve, accuracy, and F1 score was compared with that of the LR-based scoring model. Postoperative AKI developed in 1167 patients (28.4%). All the ML models had higher performance index values than the LR-based scoring model. Among them, the LightGBM model had the highest value of 0.810 (0.783–0.837). The decision curve analysis demonstrated a greater net benefit of the ML models than the LR-based scoring model over all the ranges of threshold probabilities. The application of ML algorithms improves the predictability of AKI after nephrectomy for RCC, and these models perform better than conventional LR-based models.

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