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

• Published in: International journal of intelligent information technologies Vol. 20; no. 1; pp. 1 - 22
• Main Authors: Shanmugarajeshwari, V, Ilayaraja, M
• Format: Journal Article
• Language: English
• Published: Hershey IGI Global 01.01.2024
• Subjects: Algorithms; CAI; Chronic kidney failure; Classification; Clustering; Computer assisted instruction; Data mining; Decision trees; Deep learning; Diagnosis; Equipment and supplies; Fuzzy logic; Fuzzy systems; Kidney diseases; Machine learning; Neural networks; Teaching; Unstructured data; India
• ISSN: 1548-3657; 1548-3665
• DOI: 10.4018/IJIIT.334557

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.