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Predictive analytics for chronic kidney disease using machine learning techniques
Abstract
Predictive analytics for healthcare using machine learning is a challenged task to help doctors decide the exact treatments for saving lives. In this paper, we present machine learning techniques for predicting the chronic kidney disease using clinical data. Four machine learning methods are explored including K-nearest neighbors (KNN), support vector machine (SVM), logistic regression (LR), and decision tree classifiers. These predictive models are constructed from chronic kidney disease dataset and the performance of these models are compared together in order to select the best classifier for predicting the chronic kidney disease.
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Citations
Cited by
Machine-learning-based disease diagnosis: a comprehensive review
Ahsan, M. M., Luna, S. A., & Siddique, Z. (2022) · Healthcare · doi:10.3390/healthcare10030541 · Cited by 603
Metadata
- Authors
- Charleonnan, A., Fufaung, T., Niyomwong, T., Chokchueypattanakit, W., Suwannawach, S., & Ninchawee, N.
- Venue
- 2016 Management and Innovation Technology International Conference (MITicon)
- Publisher
- IEEE
- Published
- 2016-10
- Pages
- MIT-80-MIT-83
- OpenAlex
- W2752073414
- Cited by
- 181
- References
- 0
- FWCI
- 5.94
- Type
- conference paper
- Language
- en
- Open access
- closed
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- The paper
- Charleonnan, A., Fufaung, T., Niyomwong, T., Chokchueypattanakit, W., Suwannawach, S., & Ninchawee, N. (2016). Predictive analytics for chronic kidney disease using machine learning techniques. 2016 Management and Innovation Technology International Conference (MITicon). https://doi.org/10.1109/miticon.2016.8025242
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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