Atherosclerotic Cardiovascular Disease Risk Calculation on Cleveland Dataset

The 2013 ASCVD (Atherosclerotic Cardiovascular Disease) risk score was evaluated on the Cleveland dataset, yielding key performance metrics. The score achieved an accuracy of 69.64%, indicating that approximately 70% of predictions matched actual outcomes. Precision was 63.58%, reflecting the proportion of correctly identified positive cases among all predicted positives, while recall was 79.14%, demonstrating the model’s ability to capture most actual positive cases. The F1 score, balancing precision and recall, was 70.51%, signifying a moderate trade-off between the two. Additionally, the AUC-ROC score of 70.36% suggests a fair level of discriminatory ability between positive and negative cases. These results indicate that the 2013 ASCVD risk score provides reasonable predictive performance for the Cleveland dataset, with notable strengths in recall but areas for improvement in precision and overall accuracy.

Female

Male

Performance Comparison

The performance comparison between male and female subgroups reveals notable differences, particularly in the variability of scores for the female group. For females, the model achieved an accuracy of 73.19%, slightly higher than the accuracy of 69.67% observed for males. However, the precision for females was lower at 48.88% compared to 68.75% for males, indicating that the model was less reliable in identifying true positives among predicted positives for the female subgroup. Conversely, the recall for females was significantly higher at 88.89%, compared to 77.19% for males, suggesting that the model was more effective in identifying actual positive cases among females

The F1 score, which balances precision and recall, highlights the disparity between the subgroups. Females achieved an F1 score of 62.87%, reflecting the impact of lower precision despite high recall, whereas males had a more balanced F1 score of 72.73%. The AUC-ROC scores further emphasize this difference, with females achieving 78.08% compared to 68.87% for males, indicating better overall discrimination for the female subgroup.

The wide variability in the scores for the female group, particularly the sharp contrast between high recall and low precision, underscores potential challenges in the model’s consistency when applied to different demographic subgroups. This variability suggests that further optimization or subgroup-specific adjustments may be necessary to ensure more balanced and equitable performance across genders.

Atherosclerotic Cardiovascular Disease Risk Calculation on VA Long Beach Dataset

The 2013 ASCVD (Atherosclerotic Cardiovascular Disease) risk score was evaluated on the VA Long Beach dataset, achieving an accuracy of 76.82%, indicating that over three-quarters of the predictions aligned with the actual outcomes. The precision of 78.95% highlights the model’s reliability in identifying true positives among predicted positives, while the recall of 93.75% demonstrates its ability to effectively detect the majority of actual positive cases. The F1 score, balancing precision and recall, was 85.71%, reflecting robust overall performance. However, the AUC-ROC score of 60.98% suggests limited discriminatory power between positive and negative classes, indicating room for improvement in distinguishing cases.

It is important to note that due to the small number of females in the VA Long Beach dataset, a sex-specific analysis was not feasible. This limitation restricts the ability to assess the model’s performance across different demographic subgroups and emphasizes the need for more diverse and balanced datasets in future analyses.