Predicting the Severity of Diabetes Using ECLAT Algorithm in Data Mining

DOI

10.2991/978-94-6463-433-4_26How to use a DOI?

Keywords

ECLAT algorithm; prediction; diabetes

Abstract

Diabetes is a long-term disease that damages the various parts of the human body. According to the World Health Organization (WHO) report, there was a deficiency of 12.9 million medical care workers estimated in 2035. Various Artificial Intelligence (AI) and Machine Learning (ML) classifiers were used to anticipate and diagnose diabetes. In the case of unsupervised ML classifications, data mining acts an important role in the diagnosis and prediction of the disease. Selecting legitimate classifiers clearly expands the correctness and adeptness of the proposed system. Public awareness of the disease is very poor in India. Deficient healthcare facilities lead to the growth of the disease in families. Apriori, FP growth, and ECLAT are the different types of association rule mining algorithms for the diagnosis and prediction of diabetes. Equivalence Class clustering and bottom-up LAttice Traversal (ECLAT) algorithm is used for the prediction of the severity of the diabetes in the proposed paper. The proposed work will provide a new platform for analyzing the data set for new patients and submitting an accurate prediction. Pregnancy frequency, diastolic blood pressure, diabetes pedigree function, and class distribution outcome, etc. are the parameters considered for the prediction of the severity range of diabetes. This paper aims to develop a model for an Intelligent Diabetes Prediction system using the ECLAT algorithm and reduce medical misdiagnoses by providing proper interpretation and bringing down treatment costs.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - P.Senthil Kumari
PY  - 2024
DA  - 2024/06/03
TI  - Predicting the Severity of Diabetes Using ECLAT Algorithm in Data Mining
BT  - Proceedings of the International Conference on Digital Transformation in Business: Navigating the New Frontiers Beyond Boundaries (DTBNNF 2024)
PB  - Atlantis Press
SP  - 359
EP  - 370
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-433-4_26
DO  - 10.2991/978-94-6463-433-4_26
ID  - Kumari2024
ER  -