Intelligent Hybrid System for Automated Heart Disease Prediction and Identification
DOI:
https://doi.org/10.21015/vtse.v13i2.2150Abstract
Cardiovascular disorders are major worldwide health concerns that require sophisticated and effective diagnostic instruments. An intelligent hybrid decision support system for the automated diagnosis of cardiac disorders is presented in this research. The suggested system combines the improved predictive power of the Boost Trap method with the durability of the K-fold approach. Effective cross-validation using the K-fold approach guarantees the model's applicability to a variety of datasets. At this stage, the system's reliability is increased as overfitting is decreased, and a thorough assessment of its performance is given. Moreover, by iteratively increasing the system's forecast accuracy, the Boost Trap method strengthens the decision-making process. The total diagnostic precision is increased by this algorithm by combining the strengths of numerous models using ensemble learning techniques. By combining these algorithms, a synergistic decision support system is produced that can identify cardiac diseases with high accuracy and that can also readily adjust to different data settings. The Gradient Boosted Tree algorithm achieves 94.05% accuracy on GitHub and 92.19% on Kaggle datasets. Integrating these algorithms creates a decision support system that identifies cardiac diseases across data settings. When implemented, this model provides doctors a reliable tool for diagnosis, advancing automated healthcare for cardiac conditions. By giving doctors a dependable tool for prompt and precise diagnosis, the suggested method advances automated healthcare solutions and eventually helps with the efficient treatment of cardiac conditions.
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