Enhanced Preprocessing Approach Using Ensemble Machine Learning Algorithms for Detecting Liver Disease
Abdul Quadir Md1, Sanika Kulkarni1, Christy Jackson Joshua1
1School of Computer Science and Engineering, Vellore Institute of Technology, Chennai 600127, India.
Biomedicines
|February 25, 2023
Summary
This study introduces an enhanced ensemble learning model for early liver disease detection using improved data preprocessing. The novel approach achieved high accuracy, offering a promising solution for identifying liver conditions.
Area of Science:
- Medical Informatics
- Machine Learning in Healthcare
- Computational Biology
Background:
- Global rise in liver disease incidence and mortality.
- Challenges in early liver disease detection due to limited symptoms.
- Growing effectiveness of ensemble learning over traditional algorithms.
Purpose of the Study:
- To propose a novel ensemble learning architecture for liver disease prediction.
- To enhance prediction accuracy through advanced data preprocessing techniques.
- To evaluate the performance of six ensemble algorithms on the Indian Liver Patient Dataset (ILPD).
Main Methods:
- Application of multivariate imputation for missing values.
- Utilized log1p transformation, standardization, and various scaling techniques.
- Employed univariate selection, feature importance, and correlation matrix for feature selection.
- Trained Gradient boosting, XGBoost, Bagging, Random Forest, Extra Tree, and Stacking algorithms.
Main Results:
- The proposed model demonstrated superior performance compared to existing studies.
- Extra Tree Classifier achieved the highest testing accuracy at 91.82%.
- Random Forest achieved a testing accuracy of 86.06%.
Conclusions:
- The enhanced ensemble learning model provides a robust solution for early liver disease detection.
- Advanced preprocessing significantly improves prediction accuracy.
- The developed method shows potential for real-world clinical application.
