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Microarray data is measured with numerous features, selection of the most important genes from such data sets is an essential step towards maximizing classification accuracy. Machine learning algorithms have proved to be the most effective choice in gene selection and cancer classification research. In the past, human cancer classification was basically morphological, introduction of microarray technology...
Many medical applications face a situation that the on-hand data cannot fully fit an existing predictive model or on-line tool, since these models or tools only use the most common predictors and the other valuable features collected in the current scenario are not considered altogether. On the other hand, the training data in the current scenario is not sufficient to learn a predictive model effectively...
Late diagnosis is one of the reasons that head and neck squamous cell carcinoma (HNSCC) patients experience relative five-year survival rates ranging from 40%–66%. The molecular-level differences between early and advanced stage HNSCC may provide insight into therapeutic targets and strategies. Previous bioinformatics studies have shown mixed or limited results in identifying gene and protein markers...
While cancer treatments are constantly advancing, there is still a real risk of relapse after potentially curative treatments. At the risk of adverse side effects, certain adjuvant treatments can be given to patients that are at high risk of recurrence. The challenge, however, is in finding the best tradeoff between these two extremes. Patients that are given more potent treatments, such as chemotherapy,...
Maintaining the financial sustainability of healthcare provision makes developments in e-systems of the utmost priority in healthcare. In particular, it leads to a radical review of healthcare delivery for the future as personalised, preventive, predictive and participatory, or p-Health. It is a vision that places e-systems at the core of healthcare delivery, in contrast to current practice . This...
Missing data is a given in the medical domain, so machine learning models should have satisfactory performance even when missing data occurs. Our previous work has focused on support vector machines (SVM), but we hypothesize that Bayesian networks (BN) can handle missing data better. To test the hypothesis, we trained a BN and SVM model for 2 year survival on 322 lung cancer patients and compared...
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