{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,25]],"date-time":"2025-10-25T14:19:37Z","timestamp":1761401977758,"version":"build-2065373602"},"reference-count":38,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2019,1,3]],"date-time":"2019-01-03T00:00:00Z","timestamp":1546473600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Medical service providers offer their patients high quality services in return for their trust and satisfaction. The Internet of Things (IoT) in healthcare provides different solutions to enhance the patient-physician experience. Clinical Decision-Support Systems are used to improve the quality of health services by increasing the diagnosis pace and accuracy. Based on data mining techniques and historical medical records, a classification model is built to classify patients\u2019 symptoms. In this paper, we propose a privacy-preserving clinical decision-support system based on our novel privacy-preserving single decision tree algorithm for diagnosing new symptoms without exposing patients\u2019 data to different network attacks. A homomorphic encryption cipher is used to protect users\u2019 data. In addition, the algorithm uses nonces to avoid one party from decrypting other parties\u2019 data since they all will be using the same key pair. Our simulation results have shown that our novel algorithm have outperformed the Na\u00efve Bayes algorithm by 46.46%; in addition to the effects of the key value and size on the run time. Furthermore, our model is validated by proves, which meet the privacy requirements of the hospitals\u2019 datasets, frequency of attribute values, and diagnosed symptoms.<\/jats:p>","DOI":"10.3390\/s19010142","type":"journal-article","created":{"date-parts":[[2019,1,3]],"date-time":"2019-01-03T03:36:30Z","timestamp":1546486590000},"page":"142","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["PPSDT: A Novel Privacy-Preserving Single Decision Tree Algorithm for Clinical Decision-Support Systems Using IoT Devices"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9307-1590","authenticated-orcid":false,"given":"Alia","family":"Alabdulkarim","sequence":"first","affiliation":[{"name":"Information Technology Department, King Saud University, Riyadh 11451, Saudi Arabia"},{"name":"Computer Science Department, King Saud University, Riyadh 11451, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9790-5345","authenticated-orcid":false,"given":"Mznah","family":"Al-Rodhaan","sequence":"additional","affiliation":[{"name":"Computer Science Department, King Saud University, Riyadh 11451, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2320-1692","authenticated-orcid":false,"given":"Tinghuai","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Computer Software, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2307-8201","authenticated-orcid":false,"given":"Yuan","family":"Tian","sequence":"additional","affiliation":[{"name":"Nanjing Institute of Technology, Nanjing 211167, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,1,3]]},"reference":[{"key":"ref_1","unstructured":"Habte, T.T., Saleh, H., Mohammad, B., and Ismail, M. 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