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<article xlink="http://www.w3.org/1999/xlink" dtd-version="1.0" article-type="healthcare" lang="en"><front><journal-meta><journal-id journal-id-type="publisher">IJCRR</journal-id><journal-id journal-id-type="nlm-ta">I Journ Cur Res Re</journal-id><journal-title-group><journal-title>International Journal of Current Research and Review</journal-title><abbrev-journal-title abbrev-type="pubmed">I Journ Cur Res Re</abbrev-journal-title></journal-title-group><issn pub-type="ppub">2231-2196</issn><issn pub-type="opub">0975-5241</issn><publisher><publisher-name>Radiance Research Academy</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="publisher-id">3157</article-id><article-id pub-id-type="doi"/><article-id pub-id-type="doi-url"> http://dx.doi.org/10.31782/IJCRR.2020.122332</article-id><article-categories><subj-group subj-group-type="heading"><subject>Healthcare</subject></subj-group></article-categories><title-group><article-title>Smart Health Prediction System with Data Mining&#13;
</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Wibamanto</surname><given-names>Wilson</given-names></name></contrib><contrib contrib-type="author"><name><surname>Das</surname><given-names>Debashish</given-names></name></contrib><contrib contrib-type="author"><name><surname>Chelliah</surname><given-names>Sivananthan A/L</given-names></name></contrib></contrib-group><pub-date pub-type="ppub"><day>7</day><month>12</month><year>2020</year></pub-date><volume>3)</volume><issue/><fpage>14</fpage><lpage>19</lpage><permissions><copyright-statement>This article is copyright of Popeye Publishing, 2009</copyright-statement><copyright-year>2009</copyright-year><license license-type="open-access" href="http://creativecommons.org/licenses/by/4.0/"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0) Licence. You may share and adapt the material, but must give appropriate credit to the source, provide a link to the licence, and indicate if changes were made.</license-p></license></permissions><abstract><p>Background: The digital technology era demands the world to provide an excellent health system, to ensure the citizen and community to be alive and healthy. Purpose: This study proposes the application of data mining algorithm for health prediction that can eventually shape a suitable health prediction system for patients. Although health care is available to everyone in the world, there is still no healthcare system that is completely reliable and accurate to carefully diagnose a patient on their current health issues. Even though some hospitals are well equipped to provide the best healthcare services to its citizens, some of the hospitals are still lacking in certain qualities. Consequently, patients are doubtful and uncertain when it comes to picking which hospital suits them. Problem: Numerous issues are faced by patients pertinent to hospitals such as being unable to provide medical services, insufficient number of qualified medical staffs, poor communication between doctors and patients, and unorganized health records and data. Eventually, these issues impede the opportunity for hospitals to handle both their management and their duties steadily to maintain the health of every citizen and community. Conclusion: Patients need treatment and diagnosis that are accurate and precise for them to be able to recover back for their proper health and medical staffs are required to be well-equipped in their clinical knowledge and communication skills to carefully assess their patients to ensure good health. Therefore, application of data mining in health prediction is considered in this paper as the best practice to facilitate better healthcare system.&#13;
</p></abstract><kwd-group><kwd> Clinical prediction</kwd><kwd> Data mining</kwd><kwd> Smart health system</kwd><kwd> Medical service</kwd><kwd> Health prediction</kwd><kwd> Electronic health records</kwd></kwd-group></article-meta></front></article>
