HeartGuard ML: Predictive Health Monitoring System
Project Description
"HeartGuard ML: Predictive Health Monitoring System" employs advanced machine learning techniques to predict the risk of heart disease from patient data. Developed with Python and deployed via a Flask API, it provides an easy-to-use interface for healthcare providers and individuals to obtain real-time health predictions. By facilitating early detection and personalized healthcare strategies, HeartGuard ML aims to improve patient outcomes and support proactive health management. This project underscores the power of data science in transforming preventive healthcare.
Role and Contributions
- Performed data cleaning, normalization, and feature engineering to prepare the data for model training.
- Implemented various machine learning algorithms using scikit-learn.
- Trained, validated, and fine-tuned models to achieve optimal predictive performance.
- Selected and implemented a Random Forest Classifier for final deployment.
- Developed a RESTful API using Flask to serve the machine learning model predictions.
Outcomes and Results
- The heart disease prediction model achieved a high accuracy, demonstrating its effectiveness in identifying individuals at risk of heart disease.
- The Flask-based RESTful API provided a seamless interface for users to input health data and receive predictions.
- By providing real-time predictive insights, "HeartGuard ML" enabled healthcare providers to make more informed decisions regarding patient care, potentially leading to earlier interventions and better health outcomes.
Technologies Used
- Python: Utilized for data preprocessing, machine learning model development, and API implementation.
- Pandas: Used for data manipulation and analysis.
- NumPy: Employed for numerical operations and array manipulations.
- Scikit-learn: Leveraged for building and training the machine learning models, including the Random Forest Classifier.
- Matplotlib: Used for creating visualizations to understand data distributions and model performance.
- Seaborn: Utilized for creating more complex and aesthetically pleasing statistical plots.
- Flask: A micro web framework used to develop the RESTful API for serving the machine learning model predictions.
- Joblib: Used for saving and loading the trained machine learning model.
Challenges Faced and Solutions
- Challenge: Data Quality and Preprocessing.
Solution: Employed data preprocessing techniques such as imputation for handling missing values, normalization for standardizing data formats, and outlier detection methods to clean and prepare the dataset for modeling. - Challenge: Feature Selection.
Solution: Performed exploratory data analysis (EDA) and used feature selection techniques like correlation analysis and feature importance ranking to select the most impactful features for the model.