Key result
LDGO model achieves ~95% accuracy in predicting cardiovascular disease.
Why the study?
Cardiovascular disease significantly impacts global mortality and aging, making effective risk assessment models that consider genetic, lifestyle, medical, and demographic factors critical.
Does the LDGO model improve cardiovascular disease prediction accuracy compared to standard models?
Does the LDGO model improve cardiovascular disease prediction accuracy compared to standard models?
Integrating Linear Discriminant Analysis with the Golf Optimization Algorithm yields highly accurate predictions for cardiovascular disease risk.
Hypothesis-generating for LDGO-based CVD prediction; requires prospective validation before clinical adoption.
Cardiovascular disease (CVD) significantly impacts global mortality and aging. Effective risk assessment relies on models like Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA), which consider genetic, lifestyle, medical, and demographic factors. These models improve significantly when combined with optimization techniques like the Golf Optimization Algorithm (GOA) and Leader Harris Hawk's Optimization (LHHO), leading to more accurate predictions and better early intervention. According to empirical investigation, the LDGO model, obtained by integrating the LDA model with the GOA optimizer, is the most productive model, with accuracy values of 0.948 in the training phase and 0.946 in the test phase. According to empirical investigation, the LDGO model, obtained by integrating the LDA model with the GOA optimizer, is the most productive model, with accuracy values of 0.948 in the training phase and 0.946 in the test phase.
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Kong et al. (2025) studied Cardiovascular disease. LDGO model (Linear Discriminant Analysis integrated with Golf Optimization Algorithm) was evaluated on Prediction accuracy. The LDGO model, integrating Linear Discriminant Analysis with the Golf Optimization Algorithm, achieved high accuracy for cardiovascular disease prediction (0.948 in training and 0.946 in testing).
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