Chronic Kidney Disease Prediction using Machine Learning Models

Revathy, S.; Bharathi, B.; Jeyanthi, P.; Ramesh, M. V

doi:10.35940/ijeat.a2213.109119

Cited by 55 publications

(16 citation statements)

References 10 publications

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“…Dataset. Data from the PIMA diabetes prediction model can be downloaded for free [1]. e National Institute of Diabetes and Digestive and Kidney Disease provided this information.…”

Section: Methodsmentioning

confidence: 99%

“…e information obtained from [1] was unprocessed. Because of this, several strategies including removing duplicates and null values have been used to clean the data.…”

Section: Data Cleaningmentioning

confidence: 99%

“…A prediction model that correctly classifies all the majority classes and incorrectly classifies all the minority classes will have a very high yet deceptive 95.4 percent accuracy. e cost of misclassifying individuals with diabetes will lead to severe repercussions [1][2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

“…Machine-learning classifiers are trained with the dataset to predict diabetes itself. e classifiers which are used for this research are logistic regression, Support Vector Machine, k-nearest neighbors, gradient boost, Naive Bayes, Random Forests, and voting classifier [1].…”

Section: Introductionmentioning

confidence: 99%

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Voting Classification-Based Diabetes Mellitus Prediction Using Hypertuned Machine-Learning Techniques

Mushtaq

Ramzan

Ali

et al. 2022

Mobile Information Systems

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Diabetes mellitus is a hyperglycemia-like chronic condition that is a troublesome disease. It is estimated that, according to the growing morbidity, by 2040, the world will cross 642 million diabetic patients. This means that each one of the ten adults will be diabetes-affected. Diabetes can also lead to other illnesses such as heart attacks, kidney damage, and even blindness. The prediction of diabetes in advance motivates us to develop a machine learning-based model. A dataset was obtained from the online repository for this work. The obtained dataset was imbalanced. An imbalanced dataset presents a challenge that is needed to be balanced for prediction using multiple machine learning like Tomek and SMOTE. These techniques remove necessary outliers that are incomplete in the provided dataset. These outliers are also managed using the IQR method. Additionally, this research employed a two-stage model selection methodology. In the first stage, logistic regression, Support Vector Machine, k-nearest neighbors, gradient boost, Naive Bayes, and Random Forests were applied to determine the efficiency of prediction based on patients’ preconditioning. At this stage, Random Forest was found to be the best with an accuracy of 80.7% after applying SMOTE oversampling technique to balance the dataset. In the second stage, three better-performing models were used by utilizing a voting algorithm. The results were encouraging, and the model obtained 82.0% accuracy with the default dataset and 81.7% accuracy with the balanced dataset. Naive Bayes Theorem, Gradient Boosting Classifier, and Random Forest were used as inputs to the voting algorithm.

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“…Dataset. Data from the PIMA diabetes prediction model can be downloaded for free [1]. e National Institute of Diabetes and Digestive and Kidney Disease provided this information.…”

Section: Methodsmentioning

confidence: 99%

“…e information obtained from [1] was unprocessed. Because of this, several strategies including removing duplicates and null values have been used to clean the data.…”

Section: Data Cleaningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Voting Classification-Based Diabetes Mellitus Prediction Using Hypertuned Machine-Learning Techniques

Mushtaq

Ramzan

Ali

et al. 2022

Mobile Information Systems

View full text Add to dashboard Cite

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“…The first disease considered in our study is CKD which was addressed by many researchers. Revathy et al (Revathy et al, 2019) had tried to propose a data mining framework and various classifiers on the CKD UCI dataset. Three ML algorithms such as decision tree (DT), support vector machines (SVM), and random forest (RF) were used to predict the early occurrence of CKD.…”

Section: Chronic Kidney Disease (Ckd)mentioning

confidence: 99%

Performance Evaluation of Different Machine Learning Classification Algorithms for Disease Diagnosis

Al-Hashem

Alqudah

Qananwah

2021

International Journal of E-Health and Medical Communications

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Knowledge extraction within a healthcare field is a very challenging task since we are having many problems such as noise and imbalanced datasets. They are obtained from clinical studies where uncertainty and variability are popular. Lately, a wide number of machine learning algorithms are considered and evaluated to check their validity of being used in the medical field. Usually, the classification algorithms are compared against medical experts who are specialized in certain disease diagnoses and provide an effective methodological evaluation of classifiers by applying performance metrics. The performance metrics contain four criteria: accuracy, sensitivity, and specificity forming the confusion matrix of each used algorithm. We have utilized eight different well-known machine learning algorithms to evaluate their performances in six different medical datasets. Based on the experimental results we conclude that the XGBoost and K-Nearest Neighbor classifiers were the best overall among the used datasets and signs can be used for diagnosing various diseases.

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