A Unified Hybrid Model for Cardiovascular Risk Prediction: Merging Statistical, Kernel-Based and Neural Approaches.

Area of Science:

• Cardiology
• Machine Learning
• Predictive Analytics

Background:

• Cardiovascular diseases (CVDs) remain the leading global cause of mortality.
• Traditional machine learning models struggle to accurately capture complex relationships between CVD risk factors and disease onset.
• Accurate prediction of cardiovascular risk is crucial for effective prevention and management strategies.

Purpose of the Study:

• To introduce and evaluate a novel hybrid machine learning approach for cardiovascular risk prediction (HMLCRP).
• To enhance the accuracy and reliability of CVD risk assessment by integrating diverse machine learning algorithms.
• To identify key cardiovascular risk factors for improved predictive modeling.

Main Methods:

• Developed a hybrid machine learning approach (HMLCRP) combining logistic regression (LR), support vector machines (SVMs), and neural networks (NNs).
• Incorporated critical risk factors: blood pressure, family history, stress, age, sex, cholesterol, BMI, and lifestyle choices.
• Trained and validated the HMLCRP model using benchmark datasets: Cardio statistics, Heart Disease, and Framingham Heart Study datasets.

Main Results:

• The HMLCRP demonstrated superior predictive performance compared to individual machine learning models.
• Evaluation metrics including accuracy, precision, recall, and F1-score confirmed the model's effectiveness.
• The hybrid approach successfully leveraged the strengths of LR, SVM, and NNs for robust classification and risk prediction.

Conclusions:

• The HMLCRP represents a significant advancement in personalized healthcare for cardiovascular risk management.
• This model enables proactive risk assessment and facilitates early intervention strategies to prevent CVD.
• The integration of multiple machine learning techniques offers a more accurate and reliable tool for clinical decision-making.