An Improved Machine Learning Framework for Cardiovascular Disease Prediction

Cardiovascular diseases have the highest fatality rate among the world’s most deadly syndromes. They have become stress, age, gender, cholesterol, Body Mass Index, physical inactivity, and an unhealthy diet are all key risk factors for cardiovascular disease. Based on these parameters, researchers have suggested various early diagnosis methods. However, the correctness of the supplied treatments and approaches needs considerable fine-tuning due to the cardiovascular illnesses’ intrinsic criticality and life-threatening hazards. This paper proposes a framework for accurate cardiovascular disorder prediction based on machine learning techniques. To attain the purpose, the method employs an approach called synthetic minority over-sampling (SMOTE). The benchmark datasets are used to validate the framework for achieving better accuracy, such as Recall and Accuracy. Finally, a comparison has been presented with existing state-of-the-art approaches that shows 99.16% accuracy by a collaborative model by logistic regression and KNN.