1. VALLURUPALLI NAGESWARA RAO VIGNANA JYOTHI
INSTITUTE OF ENGINEERING AND TECHNOLOGY
The "Heart Disease Prediction" project uses data analytics and machine learning to
forecast heart disease, providing clinicians a reliable tool for early identification and
personalized care.The aim is to reduce cardiovascular morbidity and mortality by
bridging technology and cardiovascular health through advanced computational
methodologies, promoting a proactive approach to patient well-being.
Abstract
CARDIOVASCULAR RISK PREDICTION
FOUNDATIONS OF DATA SCIENCE
2. METHODOLOGY
Data Collection: Compile diverse health data on physiological parameters and
historical records related to heart diseases.
Preprocessing: Cleanse and refine the dataset, addressing missing values and
outliers for optimal machine learning input.
Feature Selection: Identify key features crucial for predicting heart diseases,
optimizing model performance.
Model Development: Utilize machine learning techniques like logistic regression
or decision trees to create a predictive model.
Training and Validation: Train the model on one dataset subset, validate on
another to ensure broad applicability.
Evaluation Metrics: Assess model accuracy, precision, recall, and F1 score to
gauge effectiveness.
Deployment: Integrate the model into a user-friendly tool for clinicians, facilitating
real-time predictions within healthcare systems.
Continuous Improvement: Regularly update the model using new data for
enhanced accuracy and adaptability
