Cardiovascular Diseases Risk In People with Mental Illnesses

Abstract

ABSTRACT Cardiovascular diseases (CVD) pose a significant global health burden, with various types such as coronary heart disease, stroke, peripheral arterial disease, and aortic disease contributing to mortality and morbidity worldwide. Evidence suggested a strong association between mental health disorders and CVD, wherein conditions like mood disorders, anxiety, PTSD, and chronic stress contributed to increased risk and poorer outcomes. Conversely, CVD events could also precipitate mental health disorders, creating a complex interplay between the two domains. This study aimed to explore this relationship, identify common risk factors, and develop a predictive system for assessing CVD risk among individuals with mental illnesses. Through a review of relevant literature, the study examined prevalence rates, shared risk factors, and the impact of mental health disorders on CVD management and prognosis. Utilizing machine learning techniques, a decision support web-based system was constructed to predict CVD risk factors based on patients' mental health histories. While the scope included data collection and algorithm development, the system did not offer medical consultancy services. By illuminating the nexus between mental health and CVD, this research sought to enhance risk assessment and inform preventive interventions for vulnerable populations. This study aimed to explore this relationship, identify common risk factors, and develop a predictive system for assessing CVD risk among individuals with mental illnesses. Through a review of relevant literature, the study examined prevalence rates, shared risk factors, and the impact of mental health disorders on CVD management and prognosis. Utilizing machine learning techniques, a decision support web-based system was constructed to predict CVD risk factors based on patients' mental health histories. Machine learning algorithms analyzed comprehensive datasets to identify patterns and correlations between mental health conditions and CVD risk factors. These models were trained to recognize how specific mental health disorders and their severities influence the likelihood of developing CVD.