TY  - EJOU
AU  - Quasim, Mohammad Tabrez 
AU  - Alhuwaimel, Saad 
AU  - Shaikh, Asadullah 
AU  - Asiri, Yousef 
AU  - Rajab, Khairan 
AU  - Farkh, Rihem 
AU  - Jaloud, Khaled Al 

TI  - An Improved Machine Learning Technique with Effective Heart Disease Prediction System
T2  - Computers, Materials \& Continua

PY  - 2021
VL  - 69
IS  - 3
SN  - 1546-2226

AB  - Heart disease is the leading cause of death worldwide. Predicting heart disease is challenging because it requires substantial experience and knowledge. Several research studies have found that the diagnostic accuracy of heart disease is low. The coronary heart disorder determines the state that influences the heart valves, causing heart disease. Two indications of coronary heart disorder are strep throat with a red persistent skin rash, and a sore throat covered by tonsils or strep throat. This work focuses on a hybrid machine learning algorithm that helps predict heart attacks and arterial stiffness. At first, we achieved the component perception measured by using a hybrid cuckoo search particle swarm optimization (CSPSO) algorithm. With this perception measure, characterization and accuracy were improved, while the execution time of the proposed model was decreased. The CSPSO-deep recurrent neural network algorithm resolved issues that state-of-the-art methods face. Our proposed method offers an illustrative framework that helps predict heart attacks with high accuracy. The proposed technique demonstrates the model accuracy, which reached 0.97 with the applied dataset.
KW  - Machine learning; deep recurrent neural network; effective heart disease prediction framework

DO  - 10.32604/cmc.2021.015984