Machine learning prediction of breast cancer survival using age, sex, length of stay, mode of diagnosis and location of cancer

Breast cancer is one of the leading causes of death in females and survival depends on early diagnosis and treatment. This paper applied machine learning techniques in prediction of breast cancer survival (dead or alive) using age, sex, length of stay, mode of diagnosis and location of cancer as predictors (independent variables). The data was obtained from the outpatient department of the University of Ilorin Teaching Hospital, Ilorin, Nigeria. The sample size of 300 consists of 175 females and 25 males who were admitted at the hospital and treated for breast cancer. The patients were later discharged or died. Adaptive boosting (AdaBoost) performed best out of the data mining models used in the classification in all the three cases where the target class is average over classes, alive or dead. The AdaBoost performed best with the classification accuracy and area under curve (AUC) of 98.3% and 99.9% respectively. Furthermore, a probe on the prediction by AdaBoost showed that the probability of dead due to breast cancer is 0.47, which the length of stay hugely contributed to the high probability, location of breast cancer and mode of diagnosis contributed minimally while age and sex contributed insignificantly. The high probability of breast cancer mortality predicted in this paper is a call for concern as early detection of breast cancer, routine breast examination and breast cancer awareness are crucial in increasing the probability of survival. The results can be used to design a decision support system that can increase the chances of breast cancer survival.