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

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.

Sales Prediction of Cardiac Products by Time Series and Deep Learning

“…These estimations contribute to many industries related to the health products distribution sector and helped them by utilizing these methods, which perform as a state-of-the-art result for sales prediction. In addition, this study aims to contribute to the literature for the benefit of similar studies to be carried out [5].…”