Coronary heart disease (CHD), characterized by reduced blood flow to the heart due to the narrowing of coronary arteries, remains the leading cause of death globally. Despite advancements in early detection, disparities in the recognition and treatment outcomes of CHD persist worldwide. Previous research has shown that women with CHD are less likely than men to receive timely and accurate diagnoses, particularly when presenting critical risk factors and symptoms. While initiatives to raise awareness of these disparities have been implemented, it remains unclear whether current clinical practices have adapted to incorporate this knowledge. In this paper, we present a big data analytic solution for analyzing previously identified markers of CHD in women and assessing the association between gender-specific symptoms and diagnosis outcomes. The solution makes good use of data mining techniques in network-enabled health informatics and biomedicine domains. The implications of these findings are critical for evaluating whether healthcare practices have begun to address the nuances in CHD manifestation across genders.