Treffer: Big biomedical data analytics and knowledge data discovery in support of cancer progress prognostics.

The major goal of the paper is to share our ongoing experience in investigating the efficiency of Big biomedical analytics for cancer free survival/recurrence prognostics. We have presented the conceptual model of Big Biomedical Data digital ecosystem. The problem area under investigation is estimating the efficiency of Big Biomedical Data analytics for the case study of colorectal cancer prognostics, a disease of social significance that has a severe negative impact on quality of human life as well as incurring financial burden to the health system. The experimental dataset is composed of two joint datasets of clinical and gene expression level data. The ML models are implemented in Python in Jupiter Network programming environment. The experimental results analysis shows that the accuracy of Logistic regression classifier outperforms that of Decision tree classifiers by more than 10%. [ABSTRACT FROM AUTHOR]

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