Intelligent Decision Support for Identifying Chronic Kidney Disease Stages

Author:

Shanmugarajeshwari V.1ORCID,Ilayaraja M.1

Affiliation:

1. Kalasalingam Academy of Research and Education, India

Abstract

The decision tree classification algorithm is becoming increasingly important in machine learning (ML) technology. It is being used in a variety of fields to solve extremely complicated issues. DTCA is also utilised in medical health data to identify chronic kidney disorders such as cancer and diabetes utilising computer-aided diagnosis. Deep learning is an intelligent area of machine learning in which neural networks are used to learn unsupervised from unstructured or unlabeled data. For CKD, the DL employed the deep stacked auto-encoder and soft-max classifier techniques. Kidney illness is another condition that can lead to a variety of health problems. Random forest, SVM, C5.0, decision tree classification algorithm, C4.5, ANN, neuro-fuzzy systems, classification and clustering, DSAE, DNN, FNC, MLP are used in this study to predict and identify an early diagnosis of CKD patients using various machine and deep learning algorithms using R Studio and Python Colab software. The many stages of chronic kidney disease are identified in this paper.

Publisher

IGI Global

Subject

Decision Sciences (miscellaneous),Information Systems

Cited by 2 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

1. MRI Scans for Deep Learning-Based Chronic Nephropathy Detection: A Comparison of CNN, MobileNet, VGG16, and ResNet-50 Models;2024 5th International Conference for Emerging Technology (INCET);2024-05-24

2. Chronic Kidney Disease (Ckd) Prediction by Supervised Machine Learning Techniques;2023 3rd International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON);2023-12-29

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