Publications by authors named "M A Pourhoseingholi"
Introduction: Cancer is a significant public health issue in Iran, and its incidence has been on the rise in recent years. The objective of this study is to predict the incidence of total cancer in Iran using a Bayesian age-period-cohort (APC) model.
Methods: Utilizing age-period-cohort modeling, this study assessed the multifaceted effects of age, period, and cohort on cancer incidence during the period spanning 2005 to 2017.
Article Synopsis
- - The study analyzed COVID-19 severity and outcomes in pregnant versus nonpregnant women, matching 66 pregnant women with 107 nonpregnant women based on age and health conditions.
- - Key findings showed that pregnant women exhibited different laboratory results and radiological signs (like hazy opacities) compared to nonpregnant women, but overall severity and mortality rates were similar (4.62% for pregnant vs. 5.61% for nonpregnant).
- - The research highlights specific differences in laboratory parameters and imaging between the two groups, indicating that pregnancy does not significantly alter COVID-19 severity or mortality outcomes.
In the context of early disease detection, machine learning (ML) has emerged as a vital tool. Feature selection (FS) algorithms play a crucial role in ensuring the accuracy of predictive models by identifying the most influential variables. This study, focusing on a retrospective cohort of 4778 COVID-19 patients from Iran, explores the performance of various FS methods, including filter, embedded, and hybrid approaches, in predicting mortality outcomes.
View Article and Find Full Text PDFKnowledge, Attitude, and Practice Regarding Cardiovascular Disease among Coronary Artery Disease and Premature Coronary Artery Disease Patients Referred to Imam Hossein Hospital, Tehran, Iran.
J Tehran Heart Cent
October 2023
Background: Knowledge, attitudes, and practices (KAP) studies are widely used in public health. This study aimed to investigate and compare KAP among patients with coronary artery disease (CAD) and premature coronary artery disease (PCAD) regarding cardiovascular disease (CVD).
Methods: This cross-sectional study was conducted on 100 PCAD patients and 100 CAD patients in a general hospital in Tehran, Iran, between April and October 2022.
Background: The increasing prevalence of colorectal cancer (CRC) in Iran over the past three decades has made it a key public health burden. This study aimed to predict metastasis in CRC patients using machine learning (ML) approaches in terms of demographic and clinical factors.
Methods: This study focuses on 1,127 CRC patients who underwent appropriate treatments at Taleghani Hospital, a tertiary care facility.