My research on cardiovascular disease examines risk factors in 70,000+ individuals, utilizing machine learning for accurate prediction models. Results guide evidence-based preventive measures to combat its impact, improving societal health.
My research investigates the correlation between cardiovascular disease development and various risk factors such as lifestyle habits, clinical markers, and demographics, utilizing data from over 70,000 individuals. Machine learning models, including Logistic regression, Gaussian naive Bayes, Random Forest sampling, and Decision trees, are employed to forecast the likelihood of illness.
Preliminary findings indicate connections between cardiovascular disease and variables such as blood pressure, cholesterol, and smoking. By incorporating clinical and lifestyle characteristics, models can achieve up to 71% accuracy in predicting outcomes.
These findings will enhance our understanding of disease etiology and support the implementation of evidence-based preventive strategies to mitigate the growing health impact on society.
Risk Factors for Cardiovascular Heart Disease
- Machine learning in precision diabetes care and cardiovascular risk prediction
- Physical Activity, All-Cause and Cardiovascular Mortality, and Cardiovascular Disease
- A Systematic Review of Machine Learning and IoT Applied to the Prediction and Monitoring of Cardiovascular Diseases
