DataDriven Approaches for Early Detection and Prediction of Chronic Kidney Disease Using Machine Learning
Article No.: 67, Pages 1 - 10
Abstract
In recent years, the application of machine learning (ML) techniques for medical diagnostics has shown promising advancements. This study introduces a distinctive method for predicting chronic kidney disease (CKD) harnessing the prowess of ML. Our methodology encompasses an innovative data preprocessing approach, intricate feature engineering, and an amalgamation of ensemble techniques for model training. By evaluating our model on a dataset sourced from Kaggle, comprising 400 samples, we achieved an impressive accuracy of 98%, outperforming traditional methods. The findings underscore the potential of ML in revolutionizing CKD diagnostics, laying a foundation for further exploration in this domain.
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Published In
November 2023
1215 pages
ISBN:9798400709418
DOI:10.1145/3647444
Copyright © 2023 ACM.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 13 May 2024
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ICIMMI 2023
ICIMMI 2023: International Conference on Information Management & Machine Intelligence
November 23 - 25, 2023
Jaipur, India
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