Title: Improving e-health governance through syndromic surveillance systems and data mining in KSA

Authors: Ghada Tareq Al-Omran

Addresses: Department of Information System, Imam Mohammad bin Saud Islamic University, Riyadh, KSA

Abstract: Recently, the KSA has witnessed significant technical advances in the health sector, where local hospitals are using high-quality systems and technologies to serve patients. However, even with this great progress in healthcare systems, communication is still limited with other decision-makers in different sectors who need to access some health-related information to take the best decisions for serving patients. Therefore, this project aims to utilise the concept of electronic health governance (e-health governance) to build an automated system, which will help the health sector to know which common diseases are currently prevalent and facilitate the decision-making process by providing them with the necessary health information to help them provide the best service for patients in various fields. To do that this research will apply classification data mining techniques through using naïve Bayes classification algorithm; where this project aims to build a common diseases prediction system (CDPS) to work as syndromic surveillance system.

Keywords: data mining; electronic governance; syndromic surveillance system; SSS; naive Bayesian; common disease prediction system.

DOI: 10.1504/IJKEDM.2021.119847

International Journal of Knowledge Engineering and Data Mining, 2021 Vol.7 No.1/2, pp.39 - 52

Received: 26 Mar 2020
Accepted: 22 Nov 2020
Published online: 22 Dec 2021 *

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