Time-Efficient Hybrid Hyperparameter Tuning Approach for Cardiovascular Disease Classification

ArXi:2411.18234v2 Announce Type: replace Cardiovascular diseases (CVDs) are any serious illness of the heart, which require accurate diagnosis to prevent fatal consequences. Hyperparameter tuning plays a critical role in optimizing machine learning model performance by selecting the most suitable parameter configurations for improved accuracy, generalization, and reliability. Grid search systematically evaluates predefined hyperparameter combinations, whereas random search samples configurations randomly from the search space enabling broader exploration with reduced computational cost.