Data Mining in Cancer Research
According to the American Cancer Society, half of all men and one-third of all women in the United States will develop cancer during their lifetimes; approximately 1.5 million new cancer cases will be diagnosed in 2013. Cancer is the second most common cause of death in the United States and in the world, exceeded only by cardiovascular disease. This year, over 500,000 Americans are expected to die of cancer—more than 1,300 people a day— accounting for nearly 1 of every 4 deaths.
Cancer is a group of diseases generally characterized by uncontrolled growth and spread of abnormal cells. If the growth and/or spread are not controlled, it can result in death. Even though the exact reasons are not known, cancer is believed to be caused by both external factors (e.g., tobacco, infectious organisms, chemicals, and radiation) and internal factors (e.g., inherited mutations, hormones, immune conditions, and mutations that occur from metabolism). These causal factors may act together or in sequence to initiate or promote carcinogenesis. Cancer is treated with surgery, radiation, chemotherapy, hormone therapy, biological therapy, and targeted therapy. Survival statistics vary greatly by cancer type and stage at diagnosis.
The 5-year relative survival rate for all cancers is improving, and decline in cancer mortality has reached 20 percent in 2013, translating into the avoidance of about 1.2 million deaths from cancer since 1991. That’s more than 400 lives saved per day! The improvement in survival reflects progress in diagnosing certain cancers at an earlier stage and improvements in treatment. Further improvements are needed to prevent and treat cancer.
Even though cancer research has traditionally been clinical and biological in nature, in recent years data-driven analytic studies have become a common complement. In medical domains where data- and analytics-driven research have been applied successfully, novel research directions have been identified to further advance the clinical and biological studies. Using various types of data, including molecular, clinical, literature-based, and clinicaltrial data, along with suitable data mining tools and techniques, researchers have been able to identify novel patterns, paving the road toward a cancer-free society.
In one study, Delen (2009) used three popular data mining techniques (decision trees, artificial neural networks, and support vector machines) in conjunction with logistic regression to develop prediction models for prostate cancer survivability. The data set contained around 120,000 records and 77 variables. A k-fold cross-validation methodology was used in model building, evaluation, and comparison. The results showed that support vector models are the most accurate predictor (with a test set accuracy of 92.85%) for this domain, followed by artificial neural networks and decision trees. Furthermore, using a sensitivity–analysis-based evaluation method, the study also revealed novel patterns related to prognostic factors of prostate cancer
In a related study, Delen et al. (2004) used two data mining algorithms (artificial neural networks and decision trees) and logistic regression to develop prediction models for breast cancer survival using a large data set (more than 200,000 cases). Using a 10-fold cross-validation method to measure the unbiased estimate of the prediction models for performance comparison purposes, the results indicated that the decision tree (C5 algorithm) was the best predictor, with 93.6 percent accuracy on the holdout sample (which was the best prediction accuracy reported in the literature); followed by