ech T Press Science Computers, Materials & Continua DOI:10.32604/cmc.2021.015984 Article An Improved Machine Learning Technique with Effective Heart Disease Prediction System Mohammad Tabrez Quasim 1 , Saad Alhuwaimel 2, * , Asadullah Shaikh 3 , Yousef Asiri 3 , Khairan Rajab 3 , Rihem Farkh 4,5 and Khaled Al Jaloud 4 1 College of Computing and Information Technology, University of Bisha, Bisha, 67714, Saudi Arabia 2 King Abdulaziz City for Science and Technology, P.O. Box 6086, Riyadh, 11442, Saudi Arabia 3 College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia 4 College of Engineering, Muzahimiyah Branch, King Saud University, Riyadh, 11451, Saudi Arabia 5 Department of Electrical Engineering, Laboratory for Analysis, Conception and Control of Systems, LR-11-ES20, National Engineering School of Tunis, Tunis El Manar University, 1002, Tunisia * Corresponding Author: Saad Alhuwaimel. Email: huwaimel@kacst.edu.sa Received: 17 December 2020; Accepted: 24 April 2021 Abstract: 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 infuences 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 frst, 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 recur- rent 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. Keywords: Machine learning; deep recurrent neural network; effective heart disease prediction framework 1 Introduction Heart disease is the leading cause of death worldwide. Shortness of breath, physical short- comings, and swollen feet are typically the indicators of heart disease (HD). In some cases clinical authorities are not available to treat the coronary illness and examinations are time-consuming. HD diagnosis is generally made by a specialist who examines the patient’s clinical history and creates a physical evaluation report. However, the outcomes are often inaccurate. Therefore, it is critical to build up a non-invasive structure that depends on classifers of artifcial intelligence (AI) This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.