Determinants of developing cardiovascular disease risk with emphasis on type-2 diabetes and predictive modeling utilizing machine learning algorithms.

This research aims to enhance our comprehensive understanding of the influence of type-2 diabetes on the development of cardiovascular diseases (CVD) risk, its underlying determinants, and to construct precise predictive models capable of accurately assessing CVD risk within the context of Bangladesh. This study combined data from the 2011 and 2017 to 2018 Bangladesh Demographic and Health Surveys, focusing on individuals with hypertension. CVD development followed World Health Organization (WHO) guidelines.

Understanding and predicting pregnancy termination in Bangladesh: A comprehensive analysis using a hybrid machine learning approach.

Reproductive health issues, including unsafe pregnancy termination, remain a significant concern for women in developing nations. This study focused on investigating and predicting pregnancy termination in Bangladesh by employing a hybrid machine learning approach. The analysis used data from the Bangladesh Demographic and Health Surveys conducted in 2011, 2014, and 2017 to 2018.

Rural and urban differences in the prevalence and determinants of Type-2 diabetes in Bangladesh.

Objective: To estimate the prevalence of Type 2 Diabetes (T2D) in urban and rural settings and identify the specific risk factors for each location.

Method: We conducted this study using data from the 2017-18 Bangladesh Demographic and Health Survey (BDHS), sourced from the DHS website. The survey employed a stratified two-stage sampling method, which included 7,658 women and 7,048 men aged 18 and older who had their blood glucose levels measured.

A comparative study of machine learning algorithms for predicting domestic violence vulnerability in Liberian women.

Domestic violence against women is a prevalent in Liberia, with nearly half of women reporting physical violence. However, research on the biosocial factors contributing to this issue remains limited. This study aims to predict women's vulnerability to domestic violence using a machine learning approach, leveraging data from the Liberian Demographic and Health Survey (LDHS) conducted in 2019-2020.

An unprecedented case report of primary cardiac lymphoma exclusive to left ventricle: a diagnostic and therapeutic challenge.

Introduction: Primary cardiac lymphoma accounts for <2% of all primary cardiac tumours. It is uncommon in immunocompetent patients, often fatal and diagnosed at autopsy. Tumour usually involves the right heart chambers and pericardium.

Infection and readmission rate of cardiac implantable electronic device insertions: An observational single center study.

Background: Infection is one of the most serious complications following surgical placement of cardiac implantable electronic devices (CIEDs). Infection prevention efforts are necessary in reducing CIED infectious outcomes. These devices, however, are commonly inserted in higher risk patients, which may explain the ongoing risk of surgical site infection (SSI) in this population.

Cardiac Implantable Electronic Device Infection: From an Infection Prevention Perspective.

A cardiac implantable electronic device (CIED) is indicated for patients with severely reduced ejection fraction or with life-threatening cardiac arrhythmias. Infection related to a CIED is one of the most feared complications of this life-saving device. The rate of CIED infection has been estimated to be between 2 and 25; though evidence shows that this rate continues to rise with increasing expenditure to the patient as well as healthcare systems.