Enhanced Cardiovascular Disease Diagnosis Using Multi-Label Machine Learning and Stacking Cross-Validation

The necessity for early access to reliable models is heightened by the fact that cardiovascular disease (CVD) remains a leading global killer. This study also looks at how multi-label algorithms for machine learning may be used to better diagnose cardiovascular diseases. In this study we utilized different types of ML procedures to successfully analyze various heart disease datasets, including LR Classifier, SVM, KNN, Choice Tree, Random Forestry, and Gradient Boosting Classifiers. Conventional methods provide the groundwork for prediction, according to comparative research, but they often fall short of what is needed for peak performance and accuracy. We provide a refined model that addresses these limitations by use of a Stacking Cross-Validation (CV) Classifier. To get the best results, this model takes the best parts of many approaches and merges them. The proposed model's prediction power for cardiac illness detection is substantially enhanced by using classifiers like random forest models and Gradient Boosting. By optimizing the reproduction's generalizability, recall, accurateness, correctness, and F1-score, the Stacking CV approach ensures consistent and reliable performance across many datasets. The experimental findings show that the created Stacking CV Classifier outperforms the other methods utilized in this study with an accuracy of 98%. More accurate diagnoses of cardiovascular disease may be possible with this method, and it also outperforms the most advanced ML methods when it comes to predicting CVD in its early stages.