Volume 6, Issue 3 (July-Sep 2017)                   JCHR 2017, 6(3): 150-164 | Back to browse issues page

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Hamidi H, Moradi R. Using Mobile Health to Improve Genetic and Heart Diseases Prediction. JCHR. 2017; 6 (3) :150-164
URL: http://jhr.ssu.ac.ir/article-1-386-en.html
1- Department of Industrial Engineering, Information Technology Group, K. N.Toosi University of Technology, Tehran, Iran
Abstract:   (2696 Views)
Introduction: Mobile personal health are a rapidly growing area of health information technology. Mobile personal health users are able to manage their own health data and communicate with doctors in order to improve healthcare quality and efficiency. In recent years, information and communication technologies improvements, along with mobile Internet, offering anywhere and anytime connectivity, play a key role on modern healthcare solutions. Furthermore, Data on genetic diseases are no exception of this set of data. Therefore, appropriate algorithm and methods of data analysis should be designed.
Methods: The main objective of this research is to investigate the effective factors on more efficiency of medical data. In this article, in addition to analyzing and evaluating the best data mining algorithms used in the medical field, a new combined approach has been provided in order to predict the risk of transmitting genetic diseases. A questionnaire was developed for this task, based on the rigorous study of scientific literature concerning pregnancy and applications available on the market, with 12 data items. The data items contain calendars, genetic diseases and cardiovascular diseases information, health habits, counters, diaries, mobile features, security, backup, configuration and architectural design.
Results: Health telematics is a growing up issue that is becoming a major improvement on patient lives, especially in elderly, disabled, and chronically ill. The results of the patients clustering were obtained using the risk of transmitting genetic diseases and according to the criteria of similarity in the transmission ways as well as using a decision tree to predict whether the individual with the related characteristics is likelihood to transmit the disease or not. 300 participants were recruited, 92% routinely used and 91% owned a mobile phone. 99% were willing to receive mobile health (m-health) advice, and 79% favored mobile medication reminders.  65.2% would send home recorded information on their blood pressure, weight, medication use and lifestyle to a doctor. 81.9% trusted the confidentiality of m-health data, while 77.1% had no concerns about the privacy of their information.
Conclusion: M-health system propose healthcare delivery anytime and anywhere overcoming geographical, temporal, and even organizational barriers with low and affordable costs. This study reviewed the state-of-the-art on m-health system and technologies.
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Received: 2017/09/17 | Accepted: 2017/09/17 | Published: 2017/09/17

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