An Ensemble Feature Selection Method for Prediction of CKD

Manonmani, M.; Sarojini, B.

doi:10.1109/iccci48352.2020.9104137

Cited by 14 publications

(9 citation statements)

References 13 publications

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“…Figure 4 illustrates the feature ranking algorithm of the RFFS methodology based on feature importance and feature value. Table 2 summarizes the ranking of the features using the proposed RFFS algorithm, density‐based feature selection (DFS) method (Manonmani & Balakrishnan, n.d.), and mixed data feature selection (MDFS) model.…”

Section: Proposed Rffs Methodologymentioning

confidence: 99%

“…The LR classification strategy was shown to be the most precise in this function, including precision of roughly 97% in this analysis. Manonmani and Balakrishnan (2020) presented an ensemble feature selection method for diagnosing chronic illnesses that integrates the output of filter and wrapper techniques to provide the most discriminating characteristics. The CKD dataset is subjected to the ensembles feature extraction technique in this article.…”

Section: Related Workmentioning

confidence: 99%

“…Various classification methods are used to assess the outcomes of the ensemble feature selection approach. The suggested technique achieves a classification precision of 93% by using SVM, 97% by using Gradient Boosting, and by using CNN 97.75% for the obtained optimum feature subset, according to experimental data (Manonmani & Balakrishnan, 2020). Arumugam and Priya (2022) used machine learning techniques on clinical data to enhance feature selection for CKD predictions is a prevalent method.…”

Section: Related Workmentioning

confidence: 99%

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RFFS: Recursive random forest feature selection based ensemble algorithm for chronic kidney disease prediction

Theerthagiri

Ruby

2022

Expert Systems

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Chronic kidney disease is a global health issue that affects millions of people worldwide and causes significant social, economic, and medical issues. Several automated detection systems can diagnose chronic kidney disease. This paper proposes the recursive random forest feature selection (RFFS) based ensemble learning algorithm to diagnose chronic kidney diseases (CKD). In the decision point, decision tree-based classifiers are used. The accuracy and kappa scores are used to determine the classification results. According to the results of the proposed algorithm's performance analyses, the ensemble learning classifiers outperform other classifiers for classifying CKD. The proposed RFFS algorithm achieves 6%-40% improved prediction accuracy using the feature selection algorithm. Further, it attains 15%-39% of reduced mean square error. The performance metrics, precision, sensitivity, specificity, f1 score, and Jaccard scores, have been analysed and show greater results for the RFFS algorithm.

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Section: Proposed Rffs Methodologymentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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RFFS: Recursive random forest feature selection based ensemble algorithm for chronic kidney disease prediction

Theerthagiri

Ruby

2022

Expert Systems

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“…SVM achieved a high classification accuracy of 93%, GB achieved a high classification accuracy of 97%, and CNN achieved a high classification accuracy of 97.75% for the derived optimal feature set. The proposed work achieved a feature reduction of 62.5% for the eight features selected using the SVM and CNN classification algorithms and 66.6% for the nine features selected using the GB classification algorithm [5].…”

Section: Related Workmentioning

confidence: 99%

Feature Selection with Weighted Ensemble Ranking for Improved Classification Performance on the CSE-CIC-IDS2018 Dataset

Göcs

Johanyák

2023

Computers

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Feature selection is a crucial step in machine learning, aiming to identify the most relevant features in high-dimensional data in order to reduce the computational complexity of model development and improve generalization performance. Ensemble feature-ranking methods combine the results of several feature-selection techniques to identify a subset of the most relevant features for a given task. In many cases, they produce a more comprehensive ranking of features than the individual methods used alone. This paper presents a novel approach to ensemble feature ranking, which uses a weighted average of the individual ranking scores calculated using these individual methods. The optimal weights are determined using a Taguchi-type design of experiments. The proposed methodology significantly improves classification performance on the CSE-CIC-IDS2018 dataset, particularly for attack types where traditional average-based feature-ranking score combinations result in low classification metrics.

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“…13, No. 2, June 2024: 1489-1498 1490 [3]. Common wrapper methods include: Forward selection, backward elimination and recursive feature elimination (RFE).…”

Section: Introductionmentioning

confidence: 99%

A genetic algorithm-based feature selection approach for diabetes prediction

Kangra,

Singh

2024

IJ-AI

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<p>Genetic algorithms have emerged as a powerful optimization technique for feature selection due to their ability to search through a vast feature space efficiently. This study discusses the importance of feature selection for prediction in healthcare and prominently focuses on diabetes mellitus. Feature selection is essential for improving the performance of prediction models, by finding significant features and removing unnecessary among them. The study aims to identify the most informative subset of features. Diabetes is a chronic metabolic disorder that poses significant health challenges worldwide. For the experiment, two datasets related to diabetes were downloaded from Kaggle and the results of both (datasets) with and without feature selection using the genetic algorithm were compared. Machine learning classifiers and genetic algorithms were combined to increase the precision of diabetes risk prediction. In the preprocessing phase, feature selection, machine learning classifiers, and performance metrics methods were applied to make this study feasible. The results of the experiment showed that genetic algorithm + logistic regression i.e., 80% (accuracy) works better for PIMA diabetes, and for Germany diabetes dataset genetic algorithm + random forest and genetic algorithm + K-Nearest Neighbor i.e., 98.5% performed better than other chosen classifiers. The researchers can better comprehend the importance of feature selection in healthcare through this study.</p>

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An Ensemble Feature Selection Method for Prediction of CKD

Cited by 14 publications

References 13 publications

RFFS: Recursive random forest feature selection based ensemble algorithm for chronic kidney disease prediction

RFFS: Recursive random forest feature selection based ensemble algorithm for chronic kidney disease prediction

Feature Selection with Weighted Ensemble Ranking for Improved Classification Performance on the CSE-CIC-IDS2018 Dataset

A genetic algorithm-based feature selection approach for diabetes prediction

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