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All Journal International Journal of Informatics and Communication Technology (IJ-ICT)
Bellary, Yash Venkatesh
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Computer Science & IT

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Ensemble stacking classifier model for prediction of diabetes Bhandarkar, Mrunalini; Bendre, Varsha S.; Bellary, Yash Venkatesh; Bhole, Anuj Kiran; Bhadange, Abhishek Abasaheb
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 13, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v13i3.pp499-508

Abstract

Diabetes, being a chronic condition, possesses the capacity to instigate a global healthcare catastrophe. This condition can be managed and potentially cured with prompt diagnosis and treatment. Integrating machine learning technology with medical science enables precise prognosis of an individual’s susceptibility to diabetes. The proposed work presents the ensemble stacking classifier model. This efficient and effective diabetes prediction model predicts a patient’s diabetes risk by combining the output of multiple machine-learning techniques into a single model. The performance parameters of four distinct machine learning classification algorithms K-nearest neighbors (KNN), random forest (RF), support vector machine (SVM), and decision tree (DT) are compared in this study with those of the proposed stacked classifier model. The suggested model is developed using ensemble methods, where the previously discussed algorithms are integrated to create the base classifier layer of the stack classifier. The meta-classifier is implemented in the form of the logistic regression (LR) algorithm. Upon evaluating the performance of both the developed model and its algorithms, it is proved that the proposed model attains a testing accuracy of 88.5%, surpassing the accuracy of all baseline classification algorithms. As a result, this work determines that the ensemble stacking classifier model exhibits higher prediction accuracy than the base classifier algorithms. This finding underscores the model’s potential as a viable instrument for predicting diabetes in individuals.
Co-Authors Bendre, Varsha S. Bhadange, Abhishek Abasaheb Bhandarkar, Mrunalini Bhole, Anuj Kiran