## 1 Introduction
Major risk factors included low education level, unemployment, and obesity combined with physical inactivity (aOR = 2.19). The recommended interventions to reduce risk included health promotion, screenings and information campaigns. These results highlight the importance of targeted prevention programs for high-risk groups. A large-scale study [13] used data from 10,432 individuals aged 40 to 69 years to compare 17 measures of adiposity (ROC) to predict arterial hypertension and dyslipidemia, which are major predictors of cardiovascular disease, type 2 diabetes (T2 DM), and multi-morbidity. ROC and logistic regression curves showed that the Chinese visceral adiposity index (CVAI) and triglyceride-glucose index (TYG) better predicted arterial hypertension and multimorbidity, and body mass index (BMI) better predicted dyslipidemia. Personalized prediction of cardiovascular disease using multi-omics technologies and machine learning techniques was conducted in a study [14] that combined RNA sequencing data, single nucleotide polymorphisms (SNPs), and clinical information to create personalized risk profiles. Using trait selection methods, researchers identified 27 key transcriptomic markers and SNPs that distinguished patients with cardiovascular disease. An optimized XGboost model tuned with Bayesian hyperparameters demonstrated high accuracy. Risk assessment using [PERSON]'s additive mixture helped to explain the importance of biomarkers (RPL36 AP37 and HBA1), which was supported by the literature and demonstrated the potential of the framework to predict other diseases. An epidemiological study of cardiovascular disease (CVD) in Kashgar Prefecture, Xinjiang, northwest China was conducted to identify key risk factors. Data from 1,887,710 adults (baseline 2017) from the Kashgar prospective cohort study were analyzed, including 16 potential factors--7 demographic, 4 lifestyle, and 5 clinical factors--collected through questionnaires and medical examinations. Logistic regression models showed that all factors were significantly associated with SWD (odds ratio 1.03 to 2.99, \(p<0.05\)). Machine learning methods (Random Forest, Random Ferns, Extreme Gradient Boosting) ranked age, occupation, hypertension, exercise frequency, and dietary patterns as the five major risk factors for SWD. Stratification analyses showed consistent rankings across subgroups. This study considered cardiovascular disease as a major problem in Kashgar, and these five factors were crucial for future preventive measures [15]. A recent study [16] explored the interplay between SARS-CoV-2 (SC-2) transmission and cardiovascular complications, particularly heart attacks, using advanced mathematical modeling. A fractional-order system incorporating a fractal fractional operator (FFO) was employed to analyze local and global stability through Lyapunov functions and flip bifurcation testing. Key epidemic properties, including existence, boundedness, and positivity, were examined to ensure reliable findings. Simulations highlighted symptomatic and asymptomatic effects, offering insights into the combined impact of SC-2 and cardiovascular conditions. These approaches contributed to a deeper understanding of disease dynamics and informed future prediction and control strategies. Another study [17] focused on fractional-order modeling of cholera transmission, incorporating both asymptomatic cases and treatment interventions. The Atangana-Toufik scheme was applied to examine the system's stability, boundedness, and bifurcation properties, confirming the absence of flip bifurcation. The reproductive number (\(R_{0}\)) was computed to evaluate the transmission potential, while a sensitivity analysis highlighted key factors affecting disease spread. MATLAB R2024a simulations illustrated the role of asymptomatic measures and treatment in controlling the outbreak. These insights enhanced the understanding of cholera dynamics, supported early detection efforts, and contributed to the development of effective disease control strategies.