Enhancing Diabetes Diagnosis through the Investigation of Cost-Sensitive Learning with Ensemble Techniques
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Keywords:
Classification problem, Imbalanced datasets, Algorithm-level solutions, Ensemble techniques, Cost-sensitive learning, diabetes mellitus diagnosisAbstract
Diabetes mellitus is a prevalent chronic disease represented in the body’s un-successful insulin
effect, that appears in the elevation of the blood’s glucose levels and potential damage to many body
systems, causing the increasing of mortality rates. Early diagnosis is important for managing this illness,
and machine learning algorithms play a crucial role employing various methodologies for diabetes
detection, and in handling imbalanced data in particular.
Using diverse classification algorithms such as (Logistic Regression, Decision Tree, Random Forest,
Support Vector Machine, Convolutional Neural Network) for diabetes diagnosis and classification
demonstrate the dominance of one class and the resulting underrepresentation of the minority class.
To address this issue, cost-sensitive learning and resampling techniques are investigated in this study.
The proposed approach aimed to propose robust cost-sensitive classifiers by modifying the objective
functions of well-known algorithms. Additionally, hybrid approach of our improved Cost-sensitive
models with well used ensemble techniques like Cost-sensitive XGBoost and Cost-sensitive Random
Forest, Cost-sensitive Logistic Regression are analyzed to effectively address imbalanced classes.
To validate proposed models two imbalanced medical datasets (PIMA Indi-an, and BASEDIABET
datasets) are applied. Obtained results proves the accuracy and sensitivity of diabetes prediction models
enhancement, by reducing costly classification errors.
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References
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Similar to cost-sensitive decision trees, you can use the reference by Zadrozny et al. (2002) mentioned above.
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