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Enhanced feature selection and ensemble learning for cardiovascular disease prediction: hybrid GOL2-2 T and adaptive
S Phani Praveen1, Mohammad Kamrul Hasan2, Siti Norul Huda Sheikh Abdullah2
1Department of Computer Science and Engineering, Prasad V Potluri Siddhartha Institute of Technology, Vijayawada, India.
Insights
This study introduces a novel machine learning model for cardiovascular disease prediction, achieving 83.0% accuracy. The model effectively handles missing data and class imbalance, offering a more robust diagnostic tool.
Area of Science:
- Cardiology
- Medical Informatics
- Machine Learning
Background:
- Cardiovascular disease (CVD) remains a leading global cause of mortality.
- Current diagnostic methods for CVD often lack precision, especially for complex cases.
- There is a critical need for enhanced diagnostic tools for early CVD detection and outcome prediction.
Purpose of the Study:
- To develop and evaluate a novel machine learning approach for improving cardiovascular disease prediction.
- To address challenges in data preprocessing, including missing data, outliers, and class imbalance.
- To enhance the accuracy and reliability of CVD diagnostic models for clinical application.
Main Methods:
- Utilized Multiple Imputation by Chained Equations (MICE) for missing data imputation.
- Employed the Interquartile Range (IQR) method for outlier detection and handling.
- Applied SMOTE (Synthetic Minority Over-sampling Technique) to mitigate class imbalance.
- Introduced a Hybrid 2-Tier Grasshopper Optimization with L2 regularization (GOL2-2T) for optimal feature selection.
- Implemented an Adaboost decision fusion (ABDF) ensemble learning algorithm with a babysitting technique for hyperparameter tuning.
Main Results:
- The developed heart disease prediction model achieved an accuracy of 83.0%.
- The model demonstrated a balanced F1 score of 84.0%, indicating strong predictive performance.
- The integrated preprocessing and feature selection techniques (MICE, IQR, SMOTE, GOL2-2T) significantly enhanced model robustness and predictive capabilities.
- The ABDF algorithm effectively refined the model, proving highly effective in predicting heart disease.
Conclusions:
- The findings highlight the efficacy of advanced machine learning methodologies in medical diagnostics.
- The proposed model offers a more accurate and reliable tool for clinicians in early CVD recognition.
- Further research is warranted to validate the model's generalizability across diverse datasets and populations.
Introduction:
Global Cardiovascular disease (CVD) is still one of the leading causes of death and requires the enhancement of diagnostic methods for the effective detection of early signs and prediction of the disease outcomes. The current diagnostic tools are cumbersome and imprecise especially with complex diseases, thus emphasizing the incorporation of new machine learning applications in differential diagnosis.
Methods:
This paper presents a new machine learning approach that uses MICE for mitigating missing data, the IQR for handling outliers and SMOTE to address first imbalance distance. Additionally, to select optimal features, we introduce the Hybrid 2-Tier Grasshopper Optimization with L2 regularization methodology which we call GOL2-2T. One of the promising methods to improve the predictive modelling is an Adaboost decision fusion (ABDF) ensemble learning algorithm with babysitting technique implemented for the hyperparameters tuning. The accuracy, recall, and AUC score will be considered as the measures for assessing the model.
Results:
On the results, our heart disease prediction model yielded an accuracy of 83.0%, and a balanced F1 score of 84.0%. The integration of SMOTE, IQR outlier detection, MICE, and GOL2-2T feature selection enhances robustness while improving the predictive performance. ABDF removed the impurities in the model and elaborated its effectiveness, which proved to be high on predicting the heart disease.
Discussion:
These findings demonstrate the effectiveness of additional machine learning methodologies in medical diagnostics, including early recognition improvements and trustworthy tools for clinicians. But yes, the model's use and extent of work depends on the dataset used for it really. Further work is needed to replicate the model across different datasets and samples: as for most models, it will be important to see if the results are generalizable to populations that are not representative of the patient population that was used for the current study.
