Abstract
AbstractChronic Kidney Disease (CKD) has become a major problem in modern times, and it is dubbed the silent assassin due to its delayed signs. To overcome these critical issues, early identification may minimize the prevalence of chronic diseases, though it is quite difficult because of different kinds of limitations in the dataset. The novelty of our study is that we extracted the best features from the dataset in order to provide the best classification models for diagnosing patients with chronic kidney disease. In our study, we used CKD patients’ clinical datasets to predict CKD using some popular machine learning algorithms. After handling missing values, K-means clustering has been performed. Then feature selection was done by applying the XGBoost feature selection algorithm. After selecting features from our dataset, we have used a variety of machine learning models to determine the best classification models, including Neural Network (NN), Random Forest (RF), Support Vector Machine (SVM), Random Tree (RT), and Bagging Tree Model (BTM). Accuracy, Sensitivity, Specificity, and Kappa values were used to evaluate model performance.
Publisher
Springer Science and Business Media LLC
Reference36 articles.
1. Ohta M, Babazono T, Uchigata Y, Iwamoto Y. Comparison of the prevalence of chronic kidney disease in Japanese patients with type 1 and type 2 diabetes. Diabet Med. 2010;27(9):1017–23.
2. Dimitrijevic Z, Paunovic G, Tasic D, Mitic B, Basic D. Risk factors for urosepsis in chronic kidney disease patients with urinary tract infections. Sci Rep. 2021;11(1):1–8.
3. van der Plas E, Lullmann O, Hopkins L, Schultz JL, Nopoulos PC, Harshman LA. Associations between neurofilament light-chain protein, brain structure, and chronic kidney disease. Pediatric Res. 2021;91:135–40.
4. Couser WG, Remuzzi G, Mendis S, Tonelli M. The contribution of chronic kidney disease to the global burden of major noncommunicable diseases. Kidney Int. 2011;80(12):1258–70.
5. Phillips S, Knuchel N. Chronic kidney disease: nutrition basics. J Ren Nutr. 2011;21(4):15–7.
Cited by
18 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献