Comparative Study of Data Mining and Statistical Learning Techniques for Prediction of Cancer Survivability - Edeki, Charles; Pandya, Shardul (Richtmann Publishing, 2012)
Huge efforts are being made by computer scientists and statisticians to design and implement algorithmsand techniques for efficient storage, management, processing, and analysis of biological databases. Thedata mining and statistical learning techniques are commonly used to discover consistent and usefulpatterns in a biological dataset. These techniques are used in a computational biology and bioinformaticsfields. Computational biology and bioinformatics seeks to solve biological problems by combining aspectsof biology, computer science, mathematics, and other disciplines (Adams, Matheson & Pruim, 2008). Themain focus of this study was to expand understanding of how biologists, medical practitioners andscientists would benefit from data mining and statistical learning techniques in prediction of breast cancersurvivability and prognosis using R statistical computing tool and Weka machine learning tool (freelyavailable open source software applications). Six data mining and statistical learning techniques wereapplied to breast cancer datasets for survival analysis. The results were mixed as to which algorithm is themost optimal model, and it appeared that the performance of each algorithm depends on the size, highdimensionality of data representation and cleanliness of the dataset.