Comparative Analysis of Machine Learning Methods to Detect Chronic Kidney Disease

Roy, Madhusree Sankar; Ghosh, Ritama; Goswami, D. R.; Karthik, R.

doi:10.1088/1742-6596/1911/1/012005

Cited by 8 publications

(2 citation statements)

References 7 publications

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“…The paper aims to analyze the performance of different machine learning models for CKD diagnosis and prediction. 19. In this paper the authors used ML approaches to predict and diagnose chronic kidney disease.…”

Section: Iiliterature Survey and Existing Methodologiesmentioning

confidence: 99%

Chronic Kidney Disease Prediction Using Machine Learning

et al. 2023

IJFMR

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In today's era everyone is trying to be conscious about health although due to workload and busy schedule one gives attention to health when it shows any symptoms of some kind. However, CKD is a disease that either exhibits no symptoms at all or exhibits no signs that are particular to the condition, making it difficult to forecast, identify, and prevent such a disease and this could be led to permanently health damage, but machine learning can be hope in this problem it is best in prediction and analysis. We will employ several machine learning approaches, such as Decision Tree, KNN, Random Forest, SVM, Naive Bayes, using data that contains 24 health related attributes like age, blood pressure, sugar, glucose, taken in 2-month period of 400 patients in which 11 numeric and 14 nominal attributes in which it consists of class label named ‘Class’ which classifies patients having disease and not present. To build a model with maximum accuracy of predicting whether CKD or not and if yes then its Severity.

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Section: Iiliterature Survey and Existing Methodologiesmentioning

confidence: 99%

Chronic Kidney Disease Prediction Using Machine Learning

et al. 2023

IJFMR

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“…Chowdhury et al [9] determined the best accuracy, sensitivity, and specificity to be 0.96 (±0.01), 0.98 (±0.01), and 0.93 (±0.02), respectively. According to Roy et al [10], the extra trees classifier model provided the highest accuracy of 99.36% with one of the quickest execution periods.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Kidney Disease Detection Using Machine Learning

DIQI,

ORDIYASA,

HISWATI

2023

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This research aimed to compare the performance of ten machine learning algorithms for detecting kidney disease, utilizing data from UCI Machine Learning Repository. The algorithms tested included K-Nearest Neighbour, RBF SVM, Linear SVM, Neural Net, Decision Tree, Naïve Bayes, AdaBoost, Random Forest, Gaussian Process, and QDA. The evaluation metrics used were accuracy, precision, recall, and F1-score. The findings revealed that AdaBoost was the most effective algorithm for all evaluation metrics, achieving an accuracy, precision, recall, and F1-score of 1.00. Random Forest and RBF followed closely, while Naïve Bayes and QDA had the lowest performance. These results suggest that machine learning algorithms, especially ensemble methods such as AdaBoost, can significantly improve the accuracy and efficiency of detecting kidney disease. This can lead to better patient outcomes and reduced healthcare costs.

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