PulseTrendingJournal ClubResearchersJournalsExplore
Instagram
HomeTrendingJournal ClubExplore
Synapse
⌘+K
Synapse
April 3, 2026Journal of Education and Health PromotionOpen Access

Data mining approach to predicting of death in patients with COVID-19

View Full Paper
Ask AI
Bookmark
Share

Authors

HSHojjat SayyadiSBSaiyad BastaminejadAVAlireza Vasiee

Discussion

Loading...

Member takes

Overview

Cross-sectional study demonstrates accurate prediction of death in COVID-19 patients using machine learning models.

Key Points

  • The aim is to predict the risk of death in hospitalized COVID-19 patients using statistical modeling and machine learning techniques.
  • Cross-sectional study using data from the COVID-19 disease registration program in 2024
  • Employ various machine learning algorithms for data analysis
  • Evaluate model performance using accuracy, sensitivity, specificity, and area under the curve (AUC)
  • Identify important predictors influencing death risk
  • Highest accuracy was achieved by the logistic regression model with an AUC of 95.7
  • Key predictors of death include WBC, BUN, age, and glucose levels
  • Model accurately predicted death risk in 77.17% of cases
  • Overall accuracy was found to be 95% with a sensitivity of 88.02
  • Model can predict the risk of death at 94.54% and probability of survival at 93.81%

Cite This Study

Sayyadi et al. (2026) studied this question.

synapsesocial.com/papers/69cf5d055a333a821460a9cahttps://doi.org/10.4103/jehp.jehp_2088_24
View Full Paper
Ask AI
Bookmark
Share