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Review
. 2024 Jan 18;19(1):e0292100.
doi: 10.1371/journal.pone.0292100. eCollection 2024.

Hybrid feature selection and classification technique for early prediction and severity of diabetes type 2

Praveen Talari  1 , Bharathiraja N  2 , Gaganpreet Kaur  2 , Hani Alshahrani  3 , Mana Saleh Al Reshan  4   5 , Adel Sulaiman  3 , Asadullah Shaikh  4
Affiliations
Review

Hybrid feature selection and classification technique for early prediction and severity of diabetes type 2

Praveen Talari et al. PLoS One. .

Expression of concern in

Abstract

Diabetes prediction is an ongoing study topic in which medical specialists are attempting to forecast the condition with greater precision. Diabetes typically stays lethargic, and on the off chance that patients are determined to have another illness, like harm to the kidney vessels, issues with the retina of the eye, or a heart issue, it can cause metabolic problems and various complexities in the body. Various worldwide learning procedures, including casting a ballot, supporting, and sacking, have been applied in this review. The Engineered Minority Oversampling Procedure (Destroyed), along with the K-overlay cross-approval approach, was utilized to achieve class evening out and approve the discoveries. Pima Indian Diabetes (PID) dataset is accumulated from the UCI Machine Learning (UCI ML) store for this review, and this dataset was picked. A highlighted engineering technique was used to calculate the influence of lifestyle factors. A two-phase classification model has been developed to predict insulin resistance using the Sequential Minimal Optimisation (SMO) and SMOTE approaches together. The SMOTE technique is used to preprocess data in the model's first phase, while SMO classes are used in the second phase. All other categorization techniques were outperformed by bagging decision trees in terms of Misclassification Error rate, Accuracy, Specificity, Precision, Recall, F1 measures, and ROC curve. The model was created using a combined SMOTE and SMO strategy, which achieved 99.07% correction with 0.1 ms of runtime. The suggested system's result is to enhance the classifier's performance in spotting illness early.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Steps for traditional pre-processing of data.
Fig 2
Fig 2. Proposed architecture for diagnosing diabetes.
Fig 3
Fig 3. Description of parameters used in the dataset.
Fig 4
Fig 4. Correlation coefficient matrix.
Fig 5
Fig 5. Confusion matrix.
a. Training: BDT, b. 18.Testing- BDT, c. Training RF, d. Testing: RF, e. Training–ET, f. Testing–ET, g.Training–AB, h.Testing—AB.
Fig 6
Fig 6. Feature importance towards prediction of T2DM.
See this image and copyright information in PMC

References

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