An Integrative Paradigm for Enhanced Stroke Prediction: Synergizing XGBoost and xDeepFM Algorithms

Stroke prediction plays a crucial role in preventing and managing this debilitating condition. In this study, we address the challenge of stroke prediction using a comprehensive dataset, and propose an ensemble model that combines the power of XGBoost and xDeepFM algorithms. Our work aims to improve upon existing stroke prediction models by achieving higher accuracy and robustness. Through rigorous experimentation, we validate the effectiveness of our ensemble model using the AUC metric. Through comparing our findings with those of other models in the field, we gain valuable insights into the merits and drawbacks of various approaches. This, in turn, contributes significantly to the progress of machine learning and deep learning techniques specifically in the domain of stroke prediction

Prognostic Significance of Serum Cysteine-Rich Protein 61 in Patients with Acute Heart Failure

Background/Aims: Cyr61-cysteine-rich protein 61 (CCN1/CYR61) is a multifunctional matricellular protein involved in the regulation of fibrogenesis. Animal experiments have demonstrated that CCN1 can inhibit cardiac fibrosis in cardiac hypertrophy. However, no study has been conducted to assess the relation between serum CCN1 and prognosis of acute heart failure (AHF). Methods: We measured the serum CCN1 levels of 183 patients with AHF, and the patients were followed up for 6 months. The associations between CCN1 levels and some clinical covariates, especially left ventricular ejection fraction (LVEF), estimated glomerular filtration rate (eGFR), atrial fibrillation and age, were estimated. The AHF patients were followed up for 6 months. The endpoint was all-cause mortality. Kaplan-Meier curve analysis and multivariable Cox proportional hazards analysis were employed to evaluate the prognostic ability of CCN1. We used calibration, discrimination and reclassification to assess the mortality risk prediction of adding CCN1. Results: Serum CCN1 concentrations in AHF patients were significantly increased compared with those in individuals without AHF (237 pg/ml vs. 124.8 pg/ml, p< 0.001). CCN1 level was associated with the level of NT-proBNP (r=0.349, p< 0.001) and was not affected by LVEF, eGFR, age or atrial fibrillation in AHF patients. Importantly, Kaplan-Meier curve analysis illustrated that the AHF patients with serum CCN1 level > 260 pg/ ml had a lower survival rate (p< 0.001). Multivariate Cox hazard analysis suggests that CCN1 functions as an independent predictor of mortality for AHF patients (LgCCN1, hazard ratio 5.825, 95% confidence interval: 1.828-18.566, p=0.003). In addition, the inclusion of CCN1 in the model with NT-proBNP significantly improved the C-statistic for predicting death (0.758, p< 0.001). The integrated discrimination index was 0.019 (p< 0.001), and the net reclassification index increased significantly after addition of CCN1 (23.9%, p=0.0179). Conclusions: CCN1 is strongly predictive of 6-month mortality in patients with AHF, suggesting serum CCN1 as a promising candidate prognostic biomarker for AHF patients