Detection of Coronary Artery Using Novel Optimized Grid Search-based MLP

Iftikhar Hussain; Huma Qayyum; Raja Rizwan Javed; Farman Hassan; Auliya Ur Rahman

Authors

Iftikhar Hussain University of Engineering and Technology Taxila, Punjab Pakistan
Huma Qayyum University of Engineering and Technology Taxila, Punjab Pakistan
Raja Rizwan Javed National Defense University, Islamabad
Farman Hassan University of Engineering and Technology Taxila, Punjab Pakistan
Auliya Ur Rahman University of Engineering and Technology Taxila, Punjab Pakistan

Keywords:

heart disease, coronary artery narrowness, block vessels, heart attack, deep learning, intelligent systems.

Abstract

In recent years, we have witnessed a rapid rise in the mortality rate of people of every age due to cardiac diseases. The diagnosis of heart disease has become a challenging task in present medical research, and it depends upon the history of patients. Rapid advancements in the field of deep learning. Therefore, it is a need to develop an automated system that assists medical experts in their decision-making process. In this work, we proposed a novel optimized grid search-based multi-layer perceptron method to effectively detect heart disease patients earlier and accurately. We evaluated the performance of our method on a dataset named Public Health dataset for heart diseases. More specifically, our method obtained an accuracy of 95.12%, precision of 95.32%, recall of 95.32%, and F1-score of 95.32%. We made a comparison of our method with existing methods to check superiority and robustness of our system to detect heart disease patients. Experimental results along with comprehensive comparison with other methods illustrate that our technique has superior performance and is robust to detect heart disease patients. From the results, we can conclude that our method is reliable to be used in hospitals for the early detection of heart disease patients.

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