Abstract
This project aims to analyze the death ratio of COVID-19 patients by applying multiple logistic regression to predict outcomes based on various clinical and demographic factors. The objective is to develop a predictive model that can accurately identify patients at higher risk of mortality, enabling healthcare providers to prioritize care and resources effectively.
Introduction
The COVID-19 pandemic has posed significant challenges to global healthcare systems, with varying mortality rates reported worldwide. Understanding factors that contribute to the severity of outcomes is crucial. This project leverages statistical analysis using multiple logistic regression to explore how different variables such as age, pre-existing conditions, and treatment methods influence the probability of death in infected patients.
Existing System
Current predictive models for COVID-19 mortality are often based on simple statistical methods or machine learning models that do not always account for the interdependencies between variables. Many existing systems focus on general predictions without offering tailored results based on comprehensive datasets.
Proposed System
The proposed system aims to utilize multiple logistic regression, a method well-suited for binary outcome prediction, to analyze the death ratio among COVID-19 patients. This approach allows for adjusting multiple confounding factors simultaneously, providing a more accurate and reliable prediction model that can be updated as new data becomes available.
Methodology
- Data Collection: Gather comprehensive patient data, including demographics, medical history, treatment received, and outcomes.
- Data Preprocessing: Cleanse and prepare data for analysis, handling missing values, and normalizing data.
- Variable Selection: Identify and select significant predictors using statistical tests and domain knowledge.
- Model Development: Develop the logistic regression model, including variable selection and regularization to enhance model performance.
- Validation and Testing: Split the data into training and testing sets to validate the model’s effectiveness and adjust parameters as necessary.
- Result Analysis: Analyze the model outputs, calculate accuracy, sensitivity, and specificity, and interpret the coefficients to understand the influence of various factors on patient outcomes.
Technologies Used
- Python: For data preprocessing, analysis, and model development.
- Scikit-Learn: To implement logistic regression and other necessary statistical tools.
- Pandas & NumPy: For data manipulation and numerical operations.
- Matplotlib & Seaborn: For visualizing data and results.
- Jupyter Notebook: As an interactive coding environment for development and documentation.
