Overview This project aims to predict potential health risks during pregnancy using multiple machine learning algorithms. The model takes health-related data, including blood pressure (BP), heart rate, age of conception, and more, to provide accurate predictions about health risks. The system uses Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Decision Trees, and Random Forests to achieve high accuracy, with over 80% accuracy in all models.
Multiple ML Algorithms: Includes implementations of ANNs, SVMs, Decision Trees, and Random Forest models. Data Preprocessing: Cleans and preprocesses raw health data for better model performance. Model Evaluation: Compares the performance of multiple models to select the best one. Accurate Predictions: Provides accurate health risk information based on various health indicators. Dataset The project uses a dataset that includes features such as:
Blood Pressure (BP) Heart Rate Age of Conception Other relevant health data (e.g., weight, BMI, etc.) Installation To run the project locally, follow the steps below:
Make sure you have Python 3.x and the following libraries installed:
numpy pandas scikit-learn tensorflow (for ANNs) matplotlib seaborn (for data visualization)
Once the model is trained, you can input new health data to predict the health risks associated with pregnancy. The prediction results will be displayed, along with the corresponding risk level.
input_data = { "BP": 120, "Heart_Rate": 75, "Age_of_Conception": 29, # Add other relevant health data here }
The model will return a risk level prediction along with an explanation based on the input data.
Accuracy: Over 80% accuracy in all tested models. Best Performing Model: A detailed comparison of model performance is available in the model_comparison.py script. Contributing Feel free to fork this repository, submit issues, and create pull requests to contribute improvements or add new features.
This project is licensed under the MIT License.