Introduction
Welcome to a groundbreaking project that is poised to redefine the way we approach cardiovascular health. Our Cardiovascular Risk Assessment project represents a true data-driven revolution in the field of preventive healthcare.
Empowering Health Through Data Science
Our core mission is nothing short of empowering individuals to take charge of their well-being through data science. By harnessing the full potential of advanced analytics and machine learning, we’ve created a tool that holds the promise of saving countless lives by identifying cardiovascular risks well before they become critical.
But our project goes beyond mere numbers and predictions – it’s a testament to our commitment to the health and vitality of individuals and communities.
Meticulous Data Preprocessing: A Careful Approach
One of the cornerstones of our project is our rigorous approach to data preprocessing. We understand that the quality of data directly impacts the quality of insights:
- For numerical columns, we’ve adopted a robust strategy that replaces null values with their respective medians.
- In the case of categorical and binary columns, we’ve utilized the mode for handling null values
Unlocking Insights Through EDA
Our extensive Exploratory Data Analysis (EDA) has unearthed remarkable insights that have the potential to reshape healthcare:
- We’ve uncovered a fascinating correlation between smoking habits and cardiovascular risk, with both non-smokers and heavy smokers being at elevated risk.
- Education levels have proven to be a pivotal factor, with lower education levels associated with higher cardiovascular risk.
- Age is a significant predictor, with older individuals showing a greater likelihood of cardiovascular issues.
- Smokers at risk, on average, smoke 11 cigarettes daily.
- It’s important to note that our dataset predominantly represents older age groups, introducing potential biases
Model Training and Stellar Results
Our project has gone a step further by exploring how different data scenarios impact model performance. We’ve placed a strong emphasis on two pivotal scenarios:
Scenario 1: Balancing the training data while retaining an imbalanced test dataset. This approach mirrors real-world conditions and has yielded impressive results with an accuracy score of 87% and a recall score of 50%.
Scenario 2: Balancing both training and test data. Our concerted efforts in addressing class imbalance have led to outstanding results with an accuracy score of 100% and a recall score of 97%.
These outcomes underscore the exceptional robustness of our model in identifying individuals at risk of cardiovascular issues.
Actionable Insights and Recommendations
Our findings pave the way for concrete actions and recommendations that can contribute to a healthier society:
- Prioritize health literacy initiatives, particularly among individuals with lower education levels.
- Advocate for regular health check-ups for older adults.
- Develop tailored gender-specific health programs targeting high-risk groups.
