COVID-19 Recovery Prediction Using Machine Learning

The COVID-19 pandemic affected millions globally, with patient recovery outcomes varying significantly based on multiple factors like age, pre-existing conditions, and symptom severity. Predicting the recovery probability helps hospitals prioritize critical care, optimize resource allocation, and guide treatment decisions. Machine learning offers a powerful way to analyze patient datasets and predict recovery likelihood, improving pandemic response strategies and patient management.

Using COVID-19 patient datasets containing demographic, clinical, and symptom information, machine learning models can predict the likelihood of recovery. Models like Decision Trees, Random Forest, Gradient Boosting, or Neural Networks analyze the relationships between age, comorbidities, severity scores, and recovery outcomes. This solution enables early identification of high-risk patients and helps healthcare systems prioritize interventions dynamically during pandemic peaks.

You begin by collecting or using publicly available COVID-19 patient datasets containing clinical features like age, symptoms, oxygen levels, comorbidities, and recovery outcomes. Data preprocessing involves handling missing values and encoding categorical variables. Machine learning classification models are trained to predict recovery probabilities. Model evaluation focuses on metrics like accuracy, recall (for identifying high-risk patients), and AUC-ROC for robust performance measurement.

• Collect structured COVID-19 patient datasets including demographic and clinical attributes for analysis and modeling.
• Preprocess the data by encoding features, handling missing data, and balancing classes if necessary (oversampling/undersampling).
• Train classification models like Decision Trees, Random Forests, or XGBoost to predict recovery outcomes.
• Use evaluation metrics like recall, F1-score, and ROC curves to ensure reliable high-risk patient identification.
• Deploy the trained model as a prediction tool or integrate it with hospital management systems for proactive care planning.

• COVID-19 Open Research Dataset (CORD-19)
• COVID-19 Kaggle Dataset
• Deploy ML Models with Streamlit