Skip to content

README.md

Latest commit

 

History

History
50 lines (40 loc) · 1.63 KB

README.md

File metadata and controls

50 lines (40 loc) · 1.63 KB

COVID-19 Patient Health Prediction Using Machine Learning

Overview This project leverages machine learning techniques to predict the health status of COVID-19 patients. The goal is to optimize resource allocation in healthcare settings by accurately forecasting patient outcomes based on their clinical and demographic data.

Key Features:

  • Preprocessed data to handle missing and redundant entries.
  • Implemented supervised and deep learning methods for classification.
  • Conducted hyperparameter tuning to enhance model performance.
  • Comprehensive analysis of feature importance and model results.

Data Overview

The dataset includes features such as:

  • Demographics: Age, sex, location.
  • Medical history: Chronic diseases, previous conditions.
  • COVID-19 specific data: Symptom onset date, hospital admission date, lab results.
  • Outcome Labels: Recovered, hospitalized, deceased, etc.

Data preprocessing involved:

  • Removing or imputing missing data.
  • Standardizing features for model compatibility. -Encoding categorical variables.

Methods

Supervised Learning Models

  • Logistic Regression
  • Decision Tree
  • Random Forest
  • Support Vector Machine
  • K-Nearest Neighbors
  • Naïve Bayes
  • Ensemble methods (Adaboost, Bagging, Voting)

Deep Learning Models

  • Feedforward Neural Network

Results

Performance Metrics:

Models were evaluated using:

  • Accuracy
  • Precision
  • Recall
  • F1-score

Highlights:

Adaboost with Decision Tree base classifier achieved the best performance. Extreme Gradient Boosting (XGBoost) showed robust results across all metrics. Feedforward Neural Networks were effective for complex data patterns.