Intelligent Prediction Techniques for Chronic Kidney Disease Data Analysis

Shanmugarajeshwari V., Ilayaraja M.

Source Title: International Journal of Artificial Intelligence and Machine Learning (IJAIML)11(2)

ISSN: 2642-1577|EISSN: 2642-1585|EISBN13: 9781799864110|DOI: 10.4018/IJAIML.20210701.oa2

MLA

Shanmugarajeshwari V., and Ilayaraja M. "Intelligent Prediction Techniques for Chronic Kidney Disease Data Analysis." IJAIML vol.11, no.2 2021: pp.19-37. http://doi.org/10.4018/IJAIML.20210701.oa2

APA

Shanmugarajeshwari V. & Ilayaraja M. (2021). Intelligent Prediction Techniques for Chronic Kidney Disease Data Analysis. International Journal of Artificial Intelligence and Machine Learning (IJAIML), 11(2), 19-37. http://doi.org/10.4018/IJAIML.20210701.oa2

Chicago

Shanmugarajeshwari V., and Ilayaraja M. "Intelligent Prediction Techniques for Chronic Kidney Disease Data Analysis," International Journal of Artificial Intelligence and Machine Learning (IJAIML) 11, no.2: 19-37. http://doi.org/10.4018/IJAIML.20210701.oa2

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Abstract

Information is stored in various domains like finance, banking, hospital, education, etc. Nowadays, data stored in medical databases are growing rapidly. The proposed approach entails three parts comparable to preprocessing, attribute selection, and classification C5.0 algorithms. This work aims to design a machine-based diagnostic approach using various techniques. These algorithms improve the efficiency of mining risk factors of chronic kidney diseases, but there are also have some shortcomings. To overcome these issues and improve an effectual clinical decision support system exhausting classification methods over a large volume of the dataset for making better decisions and predictions, this paper presents grouping classification assembly through consuming the C5.0 algorithm, pointing towards assembling time to acquire great accuracy to identify an early diagnosis of chronic kidney disease patients with risk level by analyzing the chronic kidney disease dataset.