RETRACTED: Prediction of heart disease using apache spark analysing decision trees and gradient boosting algorithm

Chugh, Saryu; Selvan, K Arivu; Nadesh, R. K.

doi:10.1088/1757-899x/263/4/042078

Cited by 1 publication

(2 citation statements)

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“…Using 20-fold cross-validation and a 70-30 train-test split, tree-based RF scored 75.65 percent, Nave Bayes (NB) 71.74 percent, and KNN 65.19 percent. A decision tree and the gradient boosting method were used by [17] for prediction. The technique has a classification accuracy of 90% and computes a correlation value to determine differences between a diabetic patient and healthy person.…”

Section: Related Workmentioning

confidence: 99%

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Improving the Diabetes Diagnosis Prediction Rate Using Data Preprocessing, Data Augmentation and Recursive Feature Elimination Method

Sabitha¹,

Durgadevi²

2022

IJACSA

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Hyperglycemia is a symptom of diabetes mellitus, a metabolic condition brought on by the body's inability to produce enough insulin and respond to it. Diabetes can damage body organs if it is not adequately managed or detected in a timely manner. Many years of research into diabetes diagnosis has led to a suitable method for diabetes prediction. However, there is still scope for improvement regarding precision. The paper's primary objective is to emphasize the value of data preprocessing, feature selection, and data augmentation in disease prediction. Techniques for data preprocessing, feature selection, and data augmentation can assist classification algorithms function more effectively in the diagnosis and prediction of diabetes. A proposed method is employed for diabetes diagnosis and prediction using the PIMA Indian dataset. A systematic framework for conducting a comparison analysis based on the effectiveness of a three-category categorization model is provided in this study. The first category compares the model's performance with and without data preprocessing. The second category compares the performance of five alternative algorithms employing the Recursive Feature Elimination (RFE) feature selection method. Data augmentation is the third category; data augmentation is done with SMOTE Oversampling, and comparisons are made with and without SMOTE Oversampling. On the PIMA Indian Diabetes dataset, studies showed that data preprocessing, RFE with Random Forest Regression feature selection, and SMOTE Oversampling augmentation can produce accuracy scores of 81.25% with RF, 81.16 with DT, and 82.5% with SVC. From Six Classifiers LR, RF, DT, SVC, GNB and KNN, it is observed that RF, DT, and SVC performed better in accuracy level. The comparative study enables us to comprehend the value of data preprocessing, feature selection, and data augmentation in the disease prediction process as well as how they affect performance.

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Section: Related Workmentioning

confidence: 99%

“…Awais, M., et.al [16]. none specified RF,NB,KNN with 20 -fold cross-validation RF achieved 75.65% Selvan, K. A., et.al [17]. none specified DT,GB Achieved 90% accuracy Yang, S., et.al [18].…”

mentioning

confidence: 99%

Improving the Diabetes Diagnosis Prediction Rate Using Data Preprocessing, Data Augmentation and Recursive Feature Elimination Method

Sabitha¹,

Durgadevi²

2022

IJACSA

View full text Add to dashboard Cite

show abstract

RETRACTED: Prediction of heart disease using apache spark analysing decision trees and gradient boosting algorithm

Cited by 1 publication

References 8 publications

Improving the Diabetes Diagnosis Prediction Rate Using Data Preprocessing, Data Augmentation and Recursive Feature Elimination Method

Improving the Diabetes Diagnosis Prediction Rate Using Data Preprocessing, Data Augmentation and Recursive Feature Elimination Method

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