Classification System for Prediction of Chronic Kidney Disease Using Data Mining Techniques

Saha, Ishika; Gourisaria, Mahendra Kumar; Harshvardhan, GM

doi:10.1007/978-981-16-5689-7_38

Cited by 12 publications

(7 citation statements)

References 23 publications

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“…It uses the p-value and z-value to evaluate the performance of each individual. The calculation of z is performed using Equation (37), and then the corresponding p-value is obtained from the normal distribution table.…”

Section: Discussionmentioning

confidence: 99%

“…The value of z in this context refers to the z-score value, which is calculated using a formula represented by Equation (37). In the formula, N and M represent the quantity of models and datasets used in the experiment, respectively.…”

Section: Discussionmentioning

confidence: 99%

“…Both techniques are essential for ensuring high-quality software meets user requirements. In software fault prediction (SFP), classification [32][33][34][35][36][37] is a commonly used technique to predict whether a particular module or component of software contains a fault or defect.…”

Section: Literature Reviewmentioning

confidence: 99%

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Feature Selection Using Golden Jackal Optimization for Software Fault Prediction

et al. 2023

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A program’s bug, fault, or mistake that results in unintended results is known as a software defect or fault. Software flaws are programming errors due to mistakes in the requirements, architecture, or source code. Finding and fixing bugs as soon as they arise is a crucial goal of software development that can be achieved in various ways. So, selecting a handful of optimal subsets of features from any dataset is a prime approach. Indirectly, the classification performance can be improved through the selection of features. A novel approach to feature selection (FS) has been developed, which incorporates the Golden Jackal Optimization (GJO) algorithm, a meta-heuristic optimization technique that draws on the hunting tactics of golden jackals. Combining this algorithm with four classifiers, namely K-Nearest Neighbor, Decision Tree, Quadrative Discriminant Analysis, and Naive Bayes, will aid in selecting a subset of relevant features from software fault prediction datasets. To evaluate the accuracy of this algorithm, we will compare its performance with other feature selection methods such as FSDE (Differential Evolution), FSPSO (Particle Swarm Optimization), FSGA (Genetic Algorithm), and FSACO (Ant Colony Optimization). The result that we got from FSGJO is great for almost all the cases. For many of the results, FSGJO has given higher classification accuracy. By utilizing the Friedman and Holm tests, to determine statistical significance, the suggested strategy has been verified and found to be superior to prior methods in selecting an optimal set of attributes.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Feature Selection Using Golden Jackal Optimization for Software Fault Prediction

et al. 2023

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“…Te authors in [27] applied L1norm-based and chi-square-based FS strategies to classify breast cancer. In other CKD studies [28][29][30], principal component analysis (PCA) is utilized to extract noteworthy features from the dataset. Te authors [28] extracted 19 features using PCA and achieved the highest accuracy of 98% using the support vector machine (SVM) classifer.…”

Section: Literature Reviewmentioning

confidence: 99%

An Intelligent Diagnostic System to Analyze Early-Stage Chronic Kidney Disease for Clinical Application

Md. Ashafuddula,

Islam,

Islam

2023

Applied Computational Intelligence and Soft Computing

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Chronic kidney disease (CKD) is a progressive condition characterized by the gradual deterioration of kidney functions, potentially leading to kidney failure if not promptly diagnosed and treated. Machine learning (ML) algorithms have shown significant promise in disease diagnosis, but in healthcare, clinical data pose challenges: missing values, noisy inputs, and redundant features, affecting early-stage CKD prediction. Thus, this study presents a novel, fully automated machine learning approach to tackle these complexities by incorporating feature selection (FS) and feature space reduction (FSR) techniques, leading to a substantial enhancement of the model’s performance. A data balancing technique is also employed during preprocessing to address data imbalance issue that is commonly encountered in clinical contexts. Finally, for reliable CKD classification, an ensemble characteristics-based classifier is encouraged. The effectiveness of our approach is rigorously validated and assessed on multiple datasets, and the clinical relevancy of the strategy is evaluated on the real-world therapeutic data collected from Bangladeshi patients. The study establishes the dominance of adaptive boosting, logistic regression, and passive aggressive ML classifiers with 96.48% accuracy in forecasting unseen therapeutic CKD data, particularly in early-stage cases. Furthermore, the effectiveness of the FSR technique in reducing the prediction time significantly is revealed. The outstanding performance of the proposed model demonstrates its effectiveness in addressing the complexity of healthcare CKD data by incorporating the FS and FSR techniques. This highlights its potential as a promising computer-aided diagnosis tool for doctors, enabling early interventions and improving patient outcomes.

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“…Other algorithms like Naive Bayes, Support Vector Machine, KNN, DT, and Multilayer Perceptron showed accuracy variations from 71.56% to 96.22%. (Saha, Gourisaria, & Harshvardhan, 2022) Showed that almost all tested models had accuracies below 99.08%, with Random Forest reaching exactly 99.08%. (Purwaningsih, 2022) focused on SVM with Feature Selection (FS), finding that the combination of SVM (radial)+FS yielded the best results with an accuracy of 99.75%, followed by SVM (dot)+FS (99.50%) and SVM (polynomial)+FS (95.50%).…”

Section: Introductionmentioning

confidence: 96%

Building a Predictive Model for Chronic Kidney Disease: Integrating KNN and PSO

Widodo,

Brawijaya,

Samudi

2024

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This study examines the improvement of prediction accuracy for Chronic Kidney Disease (CKD) through the integration of the K-Nearest Neighbors (KNN) method with Particle Swarm Optimization (PSO). Amidst the rising prevalence of CKD, closely related to diabetes and hypertension, early detection of CKD becomes a significant challenge, especially in Indonesia where access to healthcare facilities and public awareness remain limited. This study utilizes the Chronic Kidney Disease dataset from the UCI Machine Learning repository, encompassing 400 patient records with 24 clinical, laboratory, and demographic variables. With the KNN method, this approach classifies data based on feature proximity, while PSO is used for feature selection and parameter optimization, enhancing the model's accuracy and efficiency in identifying CKD at early stages. The findings indicate a significant improvement in prediction accuracy, from 80.00% using KNN to 97.75% after integration with PSO. These results affirm that the combined approach of KNN and PSO holds great potential in improving early detection and management of CKD, paving the way for further research into practical applications in the healthcare field.

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Classification System for Prediction of Chronic Kidney Disease Using Data Mining Techniques

Cited by 12 publications

References 23 publications

Feature Selection Using Golden Jackal Optimization for Software Fault Prediction

Feature Selection Using Golden Jackal Optimization for Software Fault Prediction

An Intelligent Diagnostic System to Analyze Early-Stage Chronic Kidney Disease for Clinical Application

Building a Predictive Model for Chronic Kidney Disease: Integrating KNN and PSO

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