Fuzzy classifier for classification of medical data

Survival analysis is a procedure of data analysis focusing on time until an event occurs. In the medical field, predicted life expectancy is a highly significant factor in the decision making process for both the patient and the medical practitioner i.e. when making decision on palliative care and hospice referral, initiation of medications, and avoidance of aggressive therapies. The conventional statistical approach faces many challenges in handling the nature of the survival analysis datasets which often are censored data, and the difficulties in managing the complex, non-linear relationships between the prognostic factors and the patient´s tumor progression. Also the statistical approach omits the need in prediction of the patient´s prognosis since it does not take into account that all patients are individual and unique cases. The aim of this study is to develop a survival prediction model for breast cancer patients using Fuzzy Classifier (FC). The FC method applied is a new approach to classifying datasets with imbalanced and overlapping problems which is particularly effective in managing survival data since the data is widely known as imbalanced in nature and very rarely normally distributed. The results from a comparative study on FC, PNN and CART using Wisconsin breast cancer datasets are presented, where FC classification yields better results than the other two methods.