Over-sampling strategies with data cleaning for handling imbalanced problems for diabetes prediction

Nugraha, Wahyu; Maulana, Reza; Latifah,; Rahayuningsih, Panny Agustia; Nurmalasari,

doi:10.1063/5.0128407

Cited by 3 publications

(1 citation statement)

References 12 publications

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“…The noise data in this context is the minority class data that is close to the majority class, so the classification method makes biased predictions. Besides that, using hybrid sampling by combining oversampling and undersampling methods in solving unbalanced data performs better than oversampling without undersampling [18]. SMOTE-ENN hybrid sampling in this study can significantly improve the sensitivity performance [19][20].…”

Section: Resultsmentioning

confidence: 99%

A New Approach of Hybrid Sampling SMOTE and ENN to the Accuracy of Machine Learning Methods on Unbalanced Diabetes Disease Data

Hairani,

Priyanto

2023

IJACSA

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The performance of machine learning methods in disease classification is affected by the quality of the dataset, one of which is unbalanced data. One example of health data that has unbalanced data is diabetes disease data. If unbalanced data is not addressed, it can affect the performance of the classification method. Therefore, this research proposed the SMOTE-ENN approach to improving the performance of the Support Vector Machine (SVM) and Random Forest classification methods for diabetes disease prediction. The methods used in this research were SVM and Random Forest classification methods with SMOTE-ENN. The SMOTE-ENN method was used to balance the diabetes data and remove noise data adjacent to the majority and minority classes. Data that has been balanced was predicted using SVM and Random Forest methods based on the division of training and testing data with 10-fold cross-validation. The results of this study were Random Forest method with SMOTE-ENN got the best performance compared to the SVM method, such as accuracy of 95.8%, sensitivity of 98.3%, and specificity of 92.5%. In addition, the proposed method approach (Random Forest with SMOTE-ENN) also obtained the best accuracy compared to previous studies referenced. Thus, the proposed method can be adopted to predict diabetes in a health application.

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Section: Resultsmentioning

confidence: 99%

A New Approach of Hybrid Sampling SMOTE and ENN to the Accuracy of Machine Learning Methods on Unbalanced Diabetes Disease Data

Hairani,

Priyanto

2023

IJACSA

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Predicting diabetes in adults: identifying important features in unbalanced data over a 5-year cohort study using machine learning algorithm

Talebi Moghaddam,

Jahani,

Arefzadeh

et al. 2024

BMC Med Res Methodol

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Predicting Diabetes in Adults: Identifying Important Features in Unbalanced Data Over a 5-Year Cohort Study Using Machine Learning Algorithm

Moghaddam,

Jahani,

Arefzadeh

et al. 2024

Preprint

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Background Imbalanced datasets pose significant challenges in predictive modeling, leading to biased outcomes and reduced model reliability. This study addresses data imbalance in diabetes prediction using machine learning techniques. Utilizing data from the Fasa Adult Cohort Study (FACS) with a 5-year follow-up of 10,000 participants, we developed predictive models for Type 2 diabetes. Methods We employed various data-level and algorithm-level interventions, including SMOTE, ADASYN, SMOTEENN and KMeans SMOTE, paired with Random Forest, Gradient Boosting, and Multi-Layer Perceptron (MLP). Performance was evaluated using F1 score, AUC, and G-means. Results Our results show that ADASYN with MLP achieved an F1 score of 82.17 ± 3.38, AUC of 89.61 ± 2.09, and G-means of 89.15 ± 2.31. SMOTE with MLP followed closely with an F1 score of 79.85 ± 3.91, AUC of 89.7 ± 2.54, and G-means of 89.31 ± 2.78. The SMOTEENN with Random Forest combination achieved an F1 score of 78.27 ± 1.54, AUC of 87.18 ± 1.12, and G-means of 86.47 ± 1.28. Conclusion These combinations effectively address class imbalance, improving the accuracy and reliability of diabetes predictions. The findings highlight the importance of using appropriate data-balancing techniques in medical data analysis.

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Over-sampling strategies with data cleaning for handling imbalanced problems for diabetes prediction

Cited by 3 publications

References 12 publications

A New Approach of Hybrid Sampling SMOTE and ENN to the Accuracy of Machine Learning Methods on Unbalanced Diabetes Disease Data

A New Approach of Hybrid Sampling SMOTE and ENN to the Accuracy of Machine Learning Methods on Unbalanced Diabetes Disease Data

Predicting diabetes in adults: identifying important features in unbalanced data over a 5-year cohort study using machine learning algorithm

Predicting Diabetes in Adults: Identifying Important Features in Unbalanced Data Over a 5-Year Cohort Study Using Machine Learning Algorithm

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