Development of Pre-Processing for Chronic Kidney Disease Prediction Using K-Nearest Neighbors Imputer and Chi-Square

Ricky Mardianto, Ahmad Saikhu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Chronic Kidney Disease (CKD) is a condition in which the function and/or structure of the kidneys are severely damaged, resulting in an inability to filter blood as they should. This disease develops slowly and is difficult to recover from. In the early stages of CKD, symptoms often do not manifest clearly, and patients may not be aware of it. One of the primary hazards is the development of complications and mortality. The use of machine learning is seeing a growing trend in the identification of illnesses, such as CKD. Machine learning algorithms assist in identifying and predicting early-stage CKD. Early detection of CKD can provide appropriate medical treatment and medication to prevent risks from other diseases. Recent research indicates that accurately detecting CKD remains challenging due to frequently encountered invalid data and numerous missing-values. Consequently, optimal handling of missing-values within the data and the utilization of feature selection are expected to enhance the predictive quality in early CKD detection. This study employed an approach to handling missing-values using K-Nearest Neighbour (KNN) imputer and feature selection based on the use of the Chi-square test on the Chronic Kidney Disease dataset from Kaggle.com. Machine learning techniques used include Extra Tree Classifier, Random Forest, XGBoost and deep learning techniques used include TabNet and TabTransformer. The results of the experimentation showed that the Extra Tree Classifier method produced better accuracy with an accuracy of 99,25%. Thus, handling missing-values using KNN-imputer and feature selection based on the Chi-square test is a good application method for detecting early-stage CKD.

Original languageEnglish
Title of host publication2024 8th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages179-184
Number of pages6
ISBN (Electronic)9798350368970
DOIs
Publication statusPublished - 2024
Event8th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2024 - Hybrid, Yogyakarta, Indonesia
Duration: 29 Aug 202430 Aug 2024

Publication series

Name2024 8th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2024

Conference

Conference8th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2024
Country/TerritoryIndonesia
CityHybrid, Yogyakarta
Period29/08/2430/08/24

Keywords

  • KNN-Imputer
  • chi-square
  • chronic kidney disease
  • deep learning
  • machine learning
  • missing-value

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