Intelligent Decision Support for Identifying Chronic Kidney Disease Stages: Machine Learning Algorithms

Intelligent Decision Support for Identifying Chronic Kidney Disease Stages: Machine Learning Algorithms

V. Shanmugarajeshwari, M. Ilayaraja
Copyright: © 2024 |Pages: 22
DOI: 10.4018/IJIIT.334557
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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.
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Factors Of Ckd

The following are some of the factors which lead to CKD, the main cause is diabetes and others are hypertension, smoke, fatness, heart illness, family record, alcohol, and age problem.

Symptoms

Some of the warning sign is listed down, that could be variations to urinary function, plasma in the urine, bulge & pain, severe tiredness and weakness.

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