Data science for health-care: Patient condition recognition
| dc.contributor.advisor | Bagula, Antoine | |
| dc.contributor.author | Mandava, Munyaradzi | |
| dc.date.accessioned | 2020-02-19T09:55:40Z | |
| dc.date.accessioned | 2024-10-30T14:00:41Z | |
| dc.date.available | 2020-02-19T09:55:40Z | |
| dc.date.available | 2024-10-30T14:00:41Z | |
| dc.date.issued | 2019 | |
| dc.description | >Magister Scientiae - MSc | en_US |
| dc.description.abstract | The emergence of the Internet of Things (IoT) and Artificial Intelligence (AI) have elicited increased interest in many areas of our daily lives. These include health, agriculture, aviation, manufacturing, cities management and many others. In the health sector, portable vital sign monitoring devices are being developed using the IoT technology to collect patients’ vital signs in real-time. The vital sign data acquired by wearable devices is quantitative and machine learning techniques can be applied to find hidden patterns in the dataset and help the medical practitioner with decision making. There are about 30000 diseases known to man and no human being can possibly remember all of them, their relations to other diseases, their symptoms and whether the symptoms exhibited by the patients are early warnings of a fatal disease. In light of this, Medical Decision Support Systems (MDSS) can provide assistance in making these crucial assessments. In most decision support systems factors a ect each other; they can be contradictory, competitive, and complementary. All these factors contribute to the overall decision and have di erent degrees of influence [85]. However, while there is more need for automated processes to improve the health-care sector, most of MDSS and the associated devices are still under clinical trials. This thesis revisits cyber physical health systems (CPHS) with the objective of designing and implementing a data analytics platform that provides patient condition monitoring services in terms of patient prioritisation and disease identification [1]. Di erent machine learning algorithms are investigated by the platform as potential candidate for achieving patient prioritisation. These include multiple linear regression, multiple logistic regression, classification and regression decision trees, single hidden layer neural networks and deep neural networks. Graph theory concepts are used to design and implement disease identification. The data analytics platform analyses data from biomedical sensors and other descriptive data provided by the patients (this can be recent data or historical data) stored in a cloud which can be private local health Information organisation (LHIO) or belonging to a regional health information organisation (RHIO). Users of the data analytics platform consisting of medical practitioners and patients are assumed to interact with the platform through cities’ pharmacies , rural E-Health kiosks end user applications. | en_US |
| dc.identifier.uri | https://hdl.handle.net/10566/16928 | |
| dc.language.iso | en | en_US |
| dc.publisher | University of the Western Cape | en_US |
| dc.rights.holder | University of the Western Cape | en_US |
| dc.subject | Data science | en_US |
| dc.subject | Health-care | en_US |
| dc.subject | Decision trees | en_US |
| dc.subject | Graph theory | en_US |
| dc.subject | Artificial intelligence | en_US |
| dc.title | Data science for health-care: Patient condition recognition | en_US |