@Article{cmc.2020.011416,
AUTHOR = {Muhammad Adnan Khan, Sagheer Abbas, Ayesha Atta, Allah Ditta, Hani Alquhayz, Muhammad Farhan Khan, Atta-ur-Rahman, Rizwan Ali Naqvi},
TITLE = {Intelligent Cloud Based Heart Disease Prediction System  Empowered with Supervised Machine Learning},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {65},
YEAR = {2020},
NUMBER = {1},
PAGES = {139--151},
URL = {http://www.techscience.com/cmc/v65n1/39558},
ISSN = {1546-2226},
ABSTRACT = {The innovation in technologies related to health facilities today is increasingly 
helping to manage patients with different diseases. The most fatal of these is the issue of 
heart disease that cannot be detected from a naked eye, and attacks as soon as the human 
exceeds the allowed range of vital signs like pulse rate, body temperature, and blood 
pressure. The real challenge is to diagnose patients with more diagnostic accuracy and in a 
timely manner, followed by prescribing appropriate treatments and 
keeping prescription errors to a minimum. In developing countries, the domain of 
healthcare is progressing day by day using different Smart healthcare: emerging 
technologies like cloud computing, fog computing, and mobile computing. Electronic 
health records (EHRs) are used to manage the huge volume of data using cloud computing. 
That reduces the storage, processing, and retrieval cost as well as ensuring the availability 
of data. Machine learning procedures are used to extract hidden patterns and data analytics. 
In this research, a combination of cloud computing and machine learning algorithm Support 
vector machine (SVM) is used to predict heart diseases. Simulation results have shown that 
the proposed intelligent cloud-based heart disease prediction system empowered with a 
Support vector machine (SVM)-based system model gives 93.33% accuracy, which is 
better than previously published approaches.},
DOI = {10.32604/cmc.2020.011416}
}