1 research outputs found
Conformal predictors in early diagnostics of ovarian and breast cancers
The paper describes an application of a recently
developed machine learning technique called Mondrian
predictors to risk assessment of ovarian and breast
cancers. The analysis is based on mass spectrometry
profiling of human serum samples that were collected
in the United Kingdom Collaborative Trial of Ovarian
Cancer Screening. The paper describes the technique
and presents the results of classification (diagnosis)
and the corresponding measures of confidence of
the diagnostics. The main advantage of this approach
is a proven validity of prediction. The paper also describes
an approach to improve early diagnosis of ovarian
and breast cancers since the data in the United
Kingdom Collaborative Trial of Ovarian Cancer Screening
were collected over a period of seven years and do
allow to make observations of changes in human serum
over that period of time. Significance of improvement is
confirmed statistically (for up to 11 months for Ovarian
Cancer and 9 months for Breast Cancer). In addition,
the methodology allowed us to pinpoint the same mass
spectrometry peaks as previously detected as carrying
statistically significant information for discrimination
between healthy and diseased patients. The results are
discussed