Content Details | NUML Online Research Repository

Content Details

Back to Department Listing

Title MACHINE LEARNING BASED FRAMEWORK FOR HEART DISEASE DETECTION

Author(s) Rizwana Yasmeen

Abstract Cardio Vascular Diseases (CVDs), or heart diseases are one of the top-ranking causes of death worldwide. About 1 in every 4 deaths are 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 via reducing the time of obtaining a diagnosis and enhancing results. Electronic Health Records 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. In this work, a Stacking 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 the strong model outcome. The model obtained 98.4% accuracy in prediction with a precision score of 94.56%, recall of 95.6%, and F1-score of 95.89%. The performance of the model was evaluated using various metrics, including accuracy, precision, recall, F1-scores values.

Type Thesis/Dissertation MS

Faculty Engineering and Computer Science

Department Engineering

Language English

Publication Date

Subject

Publisher

Contributor(s)

Format

Identifier

Source

Relation

Coverage

Rights