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The Salp Swarm Algorithm (SSA) is a bio-inspired metaheuristic optimization technique that mimics the collective behavior of Salp chains hunting for food in the ocean. While it demonstrates competitive performance on benchmark problems, the SSA faces challenges with slow convergence and getting trapped in local optima like many population-based algorithms. To address these limitations, this study proposes the locally weighted Salp Swarm Algorithm (LWSSA), which combines two mechanisms into the standard SSA framework. First, a locally weighted approach is introduced and integrated into the SSA to guide the search toward locally promising regions. This heuristic iteratively probes high-quality solutions in the neighborhood and refines the current position. Second, a mutation operator generates new positions for Salp followers to increase randomness throughout the search. In order to assess its effectiveness, the proposed approach was evaluated against the state-of-the-art metaheuristics using standard test functions from the IEEE CEC 2021 and IEEE CEC 2017 competitions. The methodology is also applied to a risk assessment of cardiovascular disease (CVD). Seven optimization strategies of the extreme gradient boosting (XGBoost) classifier are evaluated and compared to the proposed LWSSA-XGBoost model. The proposed LWSSA-XGBoost achieves superior prediction performance with 94% F1 score, 94% recall, 93% accuracy, and 93% area under the ROC curve in comparison with state-of-the-art competitors. Overall, the experimental results demonstrate that the LWSSA enhances SSA's optimization ability and XGBoost predictive power in automated CVD risk assessment. [ABSTRACT FROM AUTHOR]