Early Prediction of Diabetes Disease Based on Data Mining Techniques

Elsadek, Salma. N; Alshehri, Lama; Alqhatani, Rawan A.; Algarni, Zainah A.; Elbadry, Linda O.; Alyahyan, Eyman

doi:10.1007/978-3-030-92600-7_4

Cited by 4 publications

(3 citation statements)

References 17 publications

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“…Finally, published works [ 32 , 33 , 34 , 35 ] based on [ 36 ] dataset. Specifically, in [ 32 ] the authors based on Naive Bayes, Logistic Regression and Random Forest algorithms and, after applying 10-fold cross-validation and percentage split (80:20) evaluation techniques, Random forest has been found to have the best accuracy in order to predict diabetes in both cases.…”

Section: Related Workmentioning

confidence: 99%

“…Multiplayer perceptron also works well with 0.96 precision value, 0.963 recall value and 0.964 F-measure value, respectively. Last, in [ 35 ], the authors based on Artificial Neural Network and Random Forest, and after applying 10-fold cross-validation, the Random Forest outperformed with an accuracy of 97.88%. To sum up, in Table 1 we summarize the aforementioned related works.…”

Section: Related Workmentioning

confidence: 99%

“…To sum up, in Table 1 we summarize the aforementioned related works. Diabetes Prediction [36] KNN Accuracy: 98.07% [34] Diabetes Prediction [36] Random Forest Accuracy, Precision, Recall, F-Measure: 97.5% [35] Diabetes Prediction [36] Random Forest Accuracy: 97.88%…”

Section: Related Workmentioning

confidence: 99%

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Data-Driven Machine-Learning Methods for Diabetes Risk Prediction

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Trigka

2022

Sensors

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Diabetes mellitus is a chronic condition characterized by a disturbance in the metabolism of carbohydrates, fats and proteins. The most characteristic disorder in all forms of diabetes is hyperglycemia, i.e., elevated blood sugar levels. The modern way of life has significantly increased the incidence of diabetes. Therefore, early diagnosis of the disease is a necessity. Machine Learning (ML) has gained great popularity among healthcare providers and physicians due to its high potential in developing efficient tools for risk prediction, prognosis, treatment and the management of various conditions. In this study, a supervised learning methodology is described that aims to create risk prediction tools with high efficiency for type 2 diabetes occurrence. A features analysis is conducted to evaluate their importance and explore their association with diabetes. These features are the most common symptoms that often develop slowly with diabetes, and they are utilized to train and test several ML models. Various ML models are evaluated in terms of the Precision, Recall, F-Measure, Accuracy and AUC metrics and compared under 10-fold cross-validation and data splitting. Both validation methods highlighted Random Forest and K-NN as the best performing models in comparison to the other models.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Data-Driven Machine-Learning Methods for Diabetes Risk Prediction

Δρίτσας

Trigka

2022

Sensors

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Detecting diabetes in an ensemble model using a unique PSO-GWO hybrid approach to hyperparameter optimization

Ulutas,

Günay,

Sahin

2024

Neural Comput & Applic

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Diabetes is a chronic medical condition that disrupts the body's normal blood sugar levels. It is essential to detect this disease at an early stage in order to prevent organ and tissue injury. This study focuses on diagnosing diabetes by leveraging ensemble learning methods, which involve combining various machine learning techniques. The goal is to create an ensemble learning model that achieves the best classification performance by employing different classifiers and combining techniques. The study explores boosting, bagging, voting, and stacking ensemble learning methods, while also introducing an approach called PSO-GWO (Particle Swarm Optimization and Grey Wolf Optimization) hybrid method for optimizing the model's hyperparameters. The model consisting of combining various classifiers in the stacking ensemble learning method provided the highest classification performance in diagnosing diabetes. The 5-fold cross-validation method is used in the study. Within the scope of the study, the highest accuracy with (98.10%) is obtained with the random forest classifier. The results of the study are presented in comparison with other studies in the literature. These findings contribute to the field of diabetes diagnosis and highlight the potential for developing more accurate and reliable diagnostic systems in the future.

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Optimized Forest Framework with A Binary Multineighborhood Artificial Bee Colony for Enhanced Diabetes Mellitus Detection

Pradhan,

Thapa,

Pradhan

et al. 2024

Int J Comput Intell Syst

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Diabetes mellitus (DM) is a common chronic condition that mainly affects older adults. It's important to identify it early to prevent complications. Machine learning is essential for early detection of DM. This article introduces a new method for detecting DM using a random forest ensemble within an optimized framework. The optimized forest framework depends on finding the best DM features, which are identified using the binary multineighborhood artificial bee colony (BMNABC) technique. During preprocessing, the BMNABC algorithm efficiently identifies important features and then inputs them into the random forest within the optimized forest framework for accurate classification. Five modern DM datasets were used to validate the suggested model. The comparison of the proposed model with other leading models revealed significant insights. The BMNABC + ODF(RFE) model demonstrated exceptional proficiency in detecting diabetes mellitus (DM) across various datasets. It achieved an accuracy of 96.36% and a sensitivity of 99.95% on the merged dataset (130 US and PIMA images). The Iranian Ministry of Health dataset showed an accuracy of 97.28% and a sensitivity of 97.12%. In the Sylhet Diabetes Hospital dataset, the accuracy and sensitivity were 96.81% and 98.07% respectively. However, on the PIMA dataset, the model displayed a nuanced performance, with an accuracy of 77.21% and a sensitivity of 68.83%. Lastly, on the questionnaire dataset, the BMNABC + ODF(RFE) model achieved an accuracy of 96.43% and a sensitivity of 97.15%. These findings emphasize the model's ability to adapt and perform effectively in different clinical environments, outperforming other models in terms of accuracy and sensitivity in detecting DM.

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Early Prediction of Diabetes Disease Based on Data Mining Techniques

Cited by 4 publications

References 17 publications

Data-Driven Machine-Learning Methods for Diabetes Risk Prediction

Data-Driven Machine-Learning Methods for Diabetes Risk Prediction

Detecting diabetes in an ensemble model using a unique PSO-GWO hybrid approach to hyperparameter optimization

Optimized Forest Framework with A Binary Multineighborhood Artificial Bee Colony for Enhanced Diabetes Mellitus Detection

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