To improve the efficiency of specific machine learning classifiers in predicting cardiovascular diseases (CVDs) using bacterial colony optimization.
Approach:
Key Findings:
SVM achieved the highest accuracy of 87.9% on the Cleveland dataset, which included specific patient demographics.
Random Forest (RF) showed consistent high performance with an accuracy of 87.64% across various datasets.
XGBoost was identified as a top performer in multiple studies, particularly when combined with PCA, enhancing its predictive capabilities.
Interpretation:
The findings indicate that advanced ML techniques, particularly when enhanced with swarm intelligence and hybrid approaches, can significantly improve the prediction of CVDs, potentially leading to better patient outcomes through timely interventions.
Limitations:
The studies reviewed varied in dataset quality and size, which may affect generalizability and the reliability of the findings.
Imbalanced datasets remain a challenge in achieving unbiased predictions, necessitating further methodological improvements.
Conclusion:
Further research is essential to refine ML models for CVD prediction, focusing on specific areas such as dataset diversity, algorithm optimization, and real-world applicability in clinical settings.
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