Genetic algorithm ensemble filter methods on kidney disease classification

• Main Authors: Huspi, Sharin Hazlin, Chong, Ke Ting
• Format: Article
• Language: English
• Published: Penerbit UTM Press 2021
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://eprints.utm.my/id/eprint/97794/1/SharinHazlin2021_GeneticAlgorithmEnsembleFilter.pdf; http://eprints.utm.my/id/eprint/97794/; http://dx.doi.org/10.11113/ijic.v11n2.345
• Institution: Universiti Teknologi Malaysia

Kidney failure will give effect to the human body, and it can lead to a series of seriously illness and even causing death. Machine learning plays important role in disease classification with high accuracy and shorter processing time as compared to clinical lab test. There are 24 attributes in the Chronic K idney Disease (CKD) clinical dataset, which is considered as too much of attributes. To improve the performance of the classification, filter feature selection methods used to reduce the dimensions of the feature and then the ensemble algorithm is used to identify the union features that selected from each filter feature selection. In this research, Random Forest (RF), XGBoost, Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Naive Bayes classification techniques were used to diagnose the CKD. The features subset that selected are different and specialised for each classifier. By implementing the proposed method irrelevant features through filter feature selection able to reduce the burden and computational cost for the genetic algorithm. Then, the genetic algorithm able to perform better and select the best subset that able to improve the performance of the classifier with less attributes. The proposed genetic algorithm union filter feature selections improve the performance of the classification algorithm. The accuracy of RF, XGBoost, KNN and SVM can achieve to 100% and NB can achieve to 99.17%. The proposed method successfully improves the performance of the classifier by using less features as compared to other previous work.