Early Detection and Diagnosis of Chronic Kidney Disease Based on Selected Predominant Features

Ullah, Zahid; Jamjoom, Mona

doi:10.1155/2023/3553216

Cited by 13 publications

(5 citation statements)

References 44 publications

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“…The selected 19 features were considered important for the classification task and were used for further analysis and model building. These selected variables are also clinically relevant to CKD, as supported by the previous literature [23,24,26,27]. The incorporation of these relevant features enhances the model's ability to accurately identify and predict cases of CKD, making it a valuable tool for early detection and effective management of the condition.…”

Section: Inputmentioning

confidence: 61%

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Enhancing the Early Detection of Chronic Kidney Disease: A Robust Machine Learning Model

Arif,

Mukheimer,

Asif

2023

BDCC

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Clinical decision-making in chronic disorder prognosis is often hampered by high variance, leading to uncertainty and negative outcomes, especially in cases such as chronic kidney disease (CKD). Machine learning (ML) techniques have emerged as valuable tools for reducing randomness and enhancing clinical decision-making. However, conventional methods for CKD detection often lack accuracy due to their reliance on limited sets of biological attributes. This research proposes a novel ML model for predicting CKD, incorporating various preprocessing steps, feature selection, a hyperparameter optimization technique, and ML algorithms. To address challenges in medical datasets, we employ iterative imputation for missing values and a novel sequential approach for data scaling, combining robust scaling, z-standardization, and min-max scaling. Feature selection is performed using the Boruta algorithm, and the model is developed using ML algorithms. The proposed model was validated on the UCI CKD dataset, achieving outstanding performance with 100% accuracy. Our approach, combining innovative preprocessing steps, the Boruta feature selection, and the k-nearest neighbors algorithm, along with a hyperparameter optimization using grid-search cross-validation (CV), demonstrates its effectiveness in enhancing the early detection of CKD. This research highlights the potential of ML techniques in improving clinical support systems and reducing the impact of uncertainty in chronic disorder prognosis.

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Section: Inputmentioning

confidence: 61%

“…They performed feature selection using the filter method and employed the k-nearest neighbor algorithm for classification. However, the study did not utilize data scaling methods or hyperparameter optimization techniques [24].…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancing the Early Detection of Chronic Kidney Disease: A Robust Machine Learning Model

Arif,

Mukheimer,

Asif

2023

BDCC

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“…However, this method is generally not recommended ( Mitchell, 1997 ; Al-Mudimigh, Ullah & Alsubaie, 2011 ; Smith & Frank, 2016 ; Brownlee, 2019 ). It is a real ML challenge to predict unseen data based on the hidden patterns of historical data that have not been seen during training ( Smith & Frank, 2016 ; Ullah & Jamjoom, 2022c ).…”

Section: Data Collectionmentioning

confidence: 99%

A smart secured framework for detecting and averting online recruitment fraud using ensemble machine learning techniques

Ullah

Jamjoom

2023

PeerJ Computer Science

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With the rise of the Internet and social media, information has become available at our fingertips. However, on the dark side, these advancements have opened doors for fraudsters. Online recruitment fraud (ORF) is one of the problems created by these modern technologies, as hundreds of thousands of applicants are victimized every year globally. Fraudsters advertise bogus jobs on online platforms and target job hunters with fake offerings such as huge salaries and desirable geographical locations. The objective of these fraudsters is to collect personal information to be misused in the future, leading to the loss of applicants’ privacy. To prevent such situations, there is a need for an automatic detecting system that can distinguish between real and fake job advertisements and preserve the applicants’ privacy. This study attempts to build a smart secured framework for detecting and preventing ORF using ensemble machine learning (ML) techniques. In this regard, four ensemble methods—AdaBoost (AB), Xtreme Gradient Boost (XGB), Voting, and Random Forest (RF)—are used to build a detection framework. The dataset used was pre-processed using several methods for cleaning and denoising in order to achieve better outcomes. The performance evaluation measures of the applied methods were accuracy, precision, sensitivity, F-measure, and ROC curves. According to these measures, AB performed best, followed by XGB, voting, and RF. In the proposed framework, AB achieved a high accuracy of 98.374%, showing its reliability for detecting and preventing ORF. The results of AB were compared to existing methods in the literature validating the reliability of the model to be significantly used for detecting ORF.

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“…The results show that XgBoost classifier has the highest performance indicators, and machine learning with predictive modeling can provide new solutions for precise detection of kidney disease. [18] The study proposes a prediction-based approach for advance detection and diagnosis of CKD using a combination of pre-processing and feature selection techniques, and various machine learning models including KNN, SVM, RF, and boosting. KNN was found to be the best-performing model with F-measure, precision, high accuracy, specificity, sensitivity and AUC score.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting Chronic Kidney Disease Using Ensemble Machine Learning Technique

Dhanwanth,

Vivek,

Abirami

et al. 2023

IJRITCC

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India is a rapidly expanding nation on a global scale. Chronic kidney disease (CKD) is a prevalent health problem internationally, and advance perception of this disease can aid prevent its stream. This research proposes an ensemble learning technique that combines three different algorithms, Logistic Regression, Gradient Boosting and Random Forest for the prediction of CKD. The performance of each algorithm was judged based on Root Mean Square Error (RMSE) and Mean Square Error (MSE) as performance metrics, and the predictions of each algorithm were combined using an ensemble learning technique. The dataset used for the study contained data on 400 individuals with 24 different features, which was pre-processed by removing missing values and normalizing the data. The combined algorithm showed a better performance with an RMSE of 0.2111 and an MSE of 0.0446, compared to individual algorithms. The proposed ensemble learning technique can be utilized as a divining for advance perception of CKD. The outcomes of the work reveal the effectiveness of the technique and its potential for improving patient outcomes by preventing the progression of CKD. Additionally, the ensemble learning technique can be applied to other predictive tasks to improve performance, indicating its broader applicability.

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Early Detection and Diagnosis of Chronic Kidney Disease Based on Selected Predominant Features

Cited by 13 publications

References 44 publications

Enhancing the Early Detection of Chronic Kidney Disease: A Robust Machine Learning Model

Enhancing the Early Detection of Chronic Kidney Disease: A Robust Machine Learning Model

A smart secured framework for detecting and averting online recruitment fraud using ensemble machine learning techniques

Forecasting Chronic Kidney Disease Using Ensemble Machine Learning Technique

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