| Issue |
E3S Web Conf.
Volume 317, 2021
The 6th International Conference on Energy, Environment, Epidemiology, and Information System (ICENIS 2021)
|
|
|---|---|---|
| Article Number | 05030 | |
| Number of page(s) | 8 | |
| Section | Information System Management and Environment | |
| DOI | https://doi.org/10.1051/e3sconf/202131705030 | |
| Published online | 05 November 2021 | |
Chronic Kidney Disease Diagnosis System using Sequential Backward Feature Selection and Artificial Neural Network
1 Master Program of Information System, Postgraduate School, Diponegoro University, Semarang
2 Department of Statistics, Faculty of Science and Mathematics, Diponegoro University, Semarang
3 Department of Mathematics, Faculty of Science and Mathematics, Diponegoro University, Semarang
* Corresponding author: imasitinoorc3424@gmail.com
The number of factors that can be categorized into the diagnosis of Chronic Kidney Disease (CKD) at an early stage makes information about the diagnosis of the disease divided into information that has many influences and has little influence. This study aims to select diagnoses in medical records with the most influential information on chronic kidney disease. The first step is to select a diagnosis with much influence by implementing the Sequential Backward Feature Selection (SBFS). This algorithm eliminates features that are considered to have little influence when compared to other features. In the second step, the features of the best diagnoses are used as input to the Artificial Neural Network (ANN) classification algorithm. The results obtained from this study are information in the form of the best diagnoses that have much influence on chronic kidney disease and the accuracy results based on the selected diagnoses. Based on the study results, 15 features are considered the best of the 18 features used to achieve 88% accuracy results. Compared with conventional methods, this method still requires consideration from the medical staff because it is not a final diagnosis for patients.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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