|Title:||Health care predictive analytics using artificial intelligence techniques|
|Subject:||Hong Kong Polytechnic University -- Dissertations|
Health services administration -- Statistical methods
Health services administration -- Decision making
|Pages:||xix, 183 pages : color illustrations|
|Abstract:||In recent years, advances in Artifcial Intelligence (AI) are opening the door for intelligent health care data prediction and decision making. Machine learning, as an increasingly popular approach to AI, has been widely used to learn directly from data, adapt independently, and produce predictive outcomes, which support doctors when encountering complex health care predictive analytics. However, traditional machine learning methods are not always perfectly working in the health feld, intrinsically due to little consideration for characteristic problems within health care data. For example, the small sample size problem is common due to complex data collection procedures and privacy concerns. Missing data is also widely encountered since most data are collected as a second-product of patient-care activities instead of following systematic research protocols. The class imbalance is another inevitable problem in the medical data as the normal class usually predominates over the disease class. To solve aforementioned issues in health care predictive analytics, this study stands on the principles of machine learning and transfer learning to develop five advanced prediction models. The frst model is an output-based transfer least squares support vector machines (LS-SVMs) model which can leverage knowledge learned from the existing prediction model to facilitate the learning process on the target domain with insuffcient data. This model overcomes the small sample size problem and improves the health care data prediction by learning knowledge from the other domain. The second model is a novel additive LS-SVMs model which can directly make predictions on missing data by simultaneously evaluating the influences on the classifcation error made by missing features. Moreover, this model can generate explanatory information for health professionals to improve the future data collection process. The third model is a transfer-based additive LS-SVMs model which can deal with missing data from a transfer learning perspective. It leverages the model knowledge learned from the complete portion of the dataset to help the learning process on the whole dataset with missing data. The proposed model can provide supplementary information for health professionals to improve the data quality via data cleaning. The forth model is a deep transfer additive LS-SVMs model called DTA-LS-SVMs and its imbalanced version called iDTA-LS-SVMs to enhance the prediction performance on the balanced and imblanced datasets. Enlightened by the deep architecture and transfer learning, the model stacks multiple additive LS-SVMs based modules layer-by-layer and embeds model transfer between adjacent modules to guarantee their consistency. The ffth model is a deep cross-output transfer LS-SVMs model called DCOT-LS-SVMs and its imbalanced version called IDCOT-LS-SVMs to improve the prediction performance on the balanced and imbalanced datasets. The cross-output transfer is used to transfer the knowledge of outcomes from the previous module to the adjacent higher layer to achieve a better learning. Moreover, modules' parameters can be randomly assigned in the proposed model which signifcantly simplifes the learning process. The proposed models are verifed using the public UCI datasets. Moreover, case studies are conducted to validate and integrate the proposed models with real world applications, including bladder cancer prognosis, prostate cancer diagnosis, and predictions of elderly quality of life (QOL). The experimental results have demonstrated that these models can enhance the prediction performances while taking the characteristic problems within health data into account, thus exhibiting potential to be widely used in the real world applications in future.|
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