Abstract
The creation of an automated system for heart disease detection was once one of the more common undertakings in the healthcare industries. For this purpose, the different types of bigdata analytics technologies are developed inthe conventional works to predict the heart disease. Still, it limits with the problems associated to the elements of high complexity, time consumption, over fitting, and mis-prediction results.Because the previous methods did not optimizethe best features, they did not give accurate results in heart attackdetection, so the system is needed to control thedeath ratio.Therefore, the proposed work objects to implement a novel Mine Blast Optimization (MBO) based Multi-Layer Perceptron Neural Network (MLPNN) technique to predict the heart attack from the given datasets. The proposed detection framework includes the stages of preprocessing, feature optimization, and classification. Here, the regression based preprocessing model is implemented to normalize theattributes for increasing the quality. Then, the MBO technique is also used to choose the relevant features based on the best optimal solution. It also helps to reduce the increase the training of classifier with reduced time consumption and high detection accuracy. Finally, the MLPNN technique is utilized to predict the classified label as whether normal ordisease affected. During analysis, the results of the proposed MBO-MLPNN technique is validated and compared by using various measures.Here the proposed method achieved 98% accuracy performance for heart attack detection than former methods
Recommended Citation
Pandian, N Rajesh; Shanth, D; and Selvaganesh, N
(2023)
"An Articulate Heart Attack Detection System Using Mine Blast Optimization (MBO) Based Multilayer Perceptron Neural Network (MLPNN) Model,"
Iraqi Journal for Computer Science and Mathematics: Vol. 4:
Iss.
2, Article 12.
DOI: https://doi.org/10.52866/ijcsm.2023.02.02.012
Available at:
https://ijcsm.researchcommons.org/ijcsm/vol4/iss2/12
