Feature elimination and stacking framework for accurate heart disease detection in IoT healthcare systems using clinical data.

Introduction: Heart disease remains a complex and critical health issue, necessitating accurate and timely detection methods. Methods: In this research, we present an advanced machine learning system designed for efficient and precise diagnosis of cardiac disease. Our approach integrates the power of Random Forest and Ada Boost classifiers, along with incorporating data pre-processing techniques such as standard scaling and Recursive Feature Elimination (RFE) for feature selection. By leveraging the ensemble learning technique of stacking, we enhance the model's predictive performance by combining the strengths of multiple classifiers. Results: The evaluation metrics results demonstrate the superior accuracy and obtained the higher performance in terms of accuracy, 99.25%. The effectiveness of our proposed system compared to baseline models. Discussion: Furthermore, the utilization of this system within IoT-enabled healthcare systems shows promising potential for improving heart disease diagnosis and ultimately enhancing patient outcomes.

DDFC: deep learning approach for deep feature extraction and classification of brain tumors using magnetic resonance imaging in E-healthcare system.

This research explores the use of gated recurrent units (GRUs) for automated brain tumor detection using MRI data. The GRU model captures sequential patterns and considers spatial information within individual MRI images and the temporal evolution of lesion characteristics. The proposed approach improves the accuracy of tumor detection using MRI images. The model's performance is benchmarked against conventional CNNs and other recurrent architectures. The research addresses interpretability concerns by employing attention mechanisms that highlight salient features contributing to the model's decisions. The proposed model attention-gated recurrent units (A-GRU) results show promising results, indicating that the proposed model surpasses the state-of-the-art models in terms of accuracy and obtained 99.32% accuracy. Due to the high predictive capability of the proposed model, we recommend it for the effective diagnosis of Brain tumors in the E-healthcare system.

Relationship Between Cardiometabolic Index in Early Pregnancy and Hypertensive Disorder Complicating Pregnancy.

Background This study aimed to examine the cardiometabolic index during early pregnancy in individuals with hypertension-complicating pregnancy, especially preeclampsia. Additionally, this study sought to determine the relationship between cardiometabolic index and the incidence of varying degrees of preeclampsia. Methodology This study included 289 pregnant women diagnosed with preeclampsia who were registered and delivered at our hospital. These women were assigned to the preeclampsia group. Additionally, a group of 289 healthy pregnant women of identical gestational ages within the same time frame was included for comparison. Clinical data on pregnancy, including body mass index (BMI), blood pressure, waistline, triglyceride levels, and cardiometabolic index, were compared between the two groups. An analysis was conducted to examine the association between early pregnancy cardiometabolic index and the occurrence of preeclampsia. Results There was a significant association between the quartile of cardiometabolic index and the proportion of preeclampsia patients (p < 0.001). Furthermore, after controlling for age and BMI, the risk of preeclampsia remained significantly elevated and was associated with the cardiometabolic index. Conclusions A positive correlation was observed between cardiometabolic index during early pregnancy and the occurrence of preeclampsia.