Healthcare Risk Prediction

Skills, Tech Stack, and Libraries

1. Skills: Predictive Modeling, Data Preprocessing, Feature Engineering, Data Visualization

2. Tech Stack: Python, SQL, AWS

3. Libraries: Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn

Approach

Objective:

I developed a machine learning system to predict the likelihood of patients developing chronic health conditions such as diabetes and heart disease. The project aimed to support healthcare providers in early diagnosis and personalized treatment plans.

Approach:

1. Data Collection and Preprocessing:

   • Utilized patient records from healthcare databases, including demographic information, clinical tests, and medical history.

   • Cleaned and standardized the data using Pandas to handle missing values, outliers, and inconsistencies in medical codes.

2. Exploratory Data Analysis (EDA):

   • Analyzed patient distributions and identified correlations between risk factors (e.g., age, BMI, cholesterol levels) and disease occurrence.

   • Visualized these relationships using Matplotlib and Seaborn to inform feature selection.

3. Feature Engineering:

   • Engineered new features such as risk scores based on clinical thresholds (e.g., high blood pressure, glucose levels).

   • Normalized numerical features and encoded categorical variables like gender and family history for better model performance.

4. Model Development:

   • Trained multiple machine learning models, including:

     • Logistic Regression for baseline performance.

     • Random Forest and Gradient Boosting for improved accuracy and interpretability.

   • Fine-tuned hyperparameters using grid search to optimize model performance.

5. Prediction and Deployment:

   • Selected the best-performing model based on evaluation metrics such as ROC-AUC, accuracy, and F1 score.

   • Deployed the model as an API using Flask, enabling integration with electronic health record (EHR) systems for real-time risk assessment.

6. Visualization and Reporting:

   • Created dashboards displaying patient risk categories, key predictors, and health trends to assist healthcare providers in decision-making.

   
   

Code Flow:

1. Extract and preprocess healthcare data using SQL and Pandas.

2. Perform EDA to uncover risk factor relationships and inform feature selection.

3. Engineer features and preprocess data for model training.

4. Train, validate, and fine-tune multiple models using Scikit-learn.

5. Deploy the final model via Flask for integration and create dashboards for stakeholders.

Results

The Healthcare Risk Prediction System achieved significant outcomes, including:

• High Predictive Accuracy: Achieved an ROC-AUC score of 0.92, effectively identifying patients at high risk of chronic conditions.

• Proactive Care: Supported early intervention strategies, reducing progression to advanced disease stages by 20%.

• Integration with EHRs: Enabled seamless integration into clinical workflows, allowing healthcare providers to access real-time risk scores.

• Personalized Insights: Highlighted key risk factors for individual patients, empowering providers to design tailored treatment plans.

This project showcased the transformative role of machine learning in improving patient outcomes and optimizing healthcare resources.

Git Link

For more information and code, visit the Git link.