A stacking classifiers model for detecting heart irregularities and predicting Cardiovascular Disease

Cardiovascular Diseases (CVDs), or heart diseases, are one of the top-ranking causes of death worldwide. About 1 in every 4 deaths is related to heart diseases, which are broadly classified as various types of abnormal heart conditions. However, diagnosis of CVDs is a time-consuming process in which data obtained from various clinical tests are manually analyzed. Therefore, new approaches for automating the detection of such irregularities in human heart conditions should be developed to provide medical practitioners with faster analysis by reducing the time of obtaining a diagnosis and enhancing results. Electronic Health Records(EHRs) are often utilized to discover useful data patterns that help improve the prediction of machine learning algorithms. Specifically, Machine Learning contributes significantly to solving issues like predictions in various domains, such as healthcare. Considering the abundance of available clinical data, there is a need to leverage such information for the betterment of humankind. Researchers have built various predictive models and systems over the years to help cardiologists and medical practitioners analyze data to attain meaningful insights. In this work, a predictive model is proposed for heart disease prediction based on the stacking of various classifiers in two levels(Base level and Meta level). Various heterogeneous learners are combined to produce strong model outcomes. The model obtained 92% accuracy in prediction with precision score of 92.6%, sensitivity of 92.6%, and specificity of 91%. The performance of the model was evaluated using various metrics, including accuracy, precision, recall, F1-scores, and area under the ROC curve values.