Assessment of Cardiovascular Disease Using Machine Learning.

Cardiovascular disease (CVD) is a prominent contributor to global mortality rates. The principal aim of this research is to employ machine learning techniques to anticipate the early actions needed to prevent the disease from progressing. The timely identification of individuals with a heightened risk of developing CVD plays an important role in implementing early interventions to impede disease progression. Machine learning techniques have shown promise in predicting CVD risk. For this paper, we propose a comprehensive CVD prediction model using ML techniques. Our approach utilizes a substantial dataset of electronic health records (EHRs) for training and validating our model. Through the incorporation of feature engineering, feature selection, and model optimization techniques, we have reached a high level of accuracy and interpretability. To evaluate the prediction of cardiovascular disease (CVD) threats, we compare the performance of various popular ML algorithms, such as logistic regression, random forest, and Support Vector Machine. Our findings point towards that our proposed model improve on existing approaches in regard of both accuracy and efficiency. This model can efficiently recognize individuals with an elevated risk of developing CVD, enabling early interventions to prevent the onset and progression of the disease. Additionally, we perform a acatalectic analysis of the features that contribute most to the assessment of CVD risk, providing insights into the underlying mechanisms of the disease. We also evaluate the robustness of our model by testing its performance on a separate dataset. Furthermore, we discuss the clinical implications of our proposed model, highlighting the potential benefits of using ML techniques in identifying individuals at high risk of developing CVD. Our model can aid in personalized medicine and facilitate the delivery of targeted interventions to high-risk individuals, thereby improving patient outcomes and reducing healthcare costs. When it comes to treating severe stages of cardiovascular disease, preventive treatments are typically more economical. Our approach can assist in lessening the financial burden related to CVD by lowering hospital stays, ER visits, and long-term care expenses via early detection of high-risk individuals and implementation of focused therapies. In summary, our model presents a robust solution for utilizing machine learning techniques to envisage the risk of cardiovascular disease (CVD). Our study targets to provide the expanding field of research regarding the application of machine learning in healthcare. The insights extended from our findings hold significant potential for enhancing the anticipation and treatment of CVD. [ABSTRACT FROM AUTHOR]