Addressing Class Imbalance in Healthcare Data: Machine Learning Solutions for Age-Related Macular Degeneration and Preeclampsia
Keywords:
Healthcare Domain, Class Imbalance, Ensemble Classifiers, Diagnostic Decision-Making, Personalized Medicine, Machine Learning TechniquesAbstract
The use of machine learning in healthcare has
transformed the way diseases are diagnosed and treatments
are optimized. However, medical databases often lack balanced
data due to challenges in data collection caused by privacy
regulations. Certain health conditions are underrepresented,
which hampers machine learning performance. To address this
problem, a hybrid approach has been proposed that combines
the Synthetic Minority Oversampling Technique (SMOTE) with
undersampling and uses two specific techniques tailored for
imbalanced datasets. Comparative evaluations were conducted
using various thresholds to reduce one class and employing
Balanced Accuracy to mitigate bias toward the majority class,
with popular machine learning methods. The results showed
that Balanced Bagging and Balanced Random Forest consistently
outperformed other methods, performing the best with
an average ranking of 1.42 and 3.58 out of 32 configurations
in the two datasets, respectively. Tree-based approaches such
as Random Forest and Gradient Boosting demonstrated similar
effectiveness, emphasizing the power of aggregating predictions
from multiple trees to reduce bias. Notably, undersampling and
SMOTE proved advantageous for non-tree-based models like
KNN, SVM, and Logistic Regression showcasing their usefulness
across different algorithms. This study provides a robust solution
for handling imbalanced datasets in healthcare, which could
potentially optimize healthcare interventions and improve patient
outcomes and care.
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