A Supervised Learning Based Recommender System for Breast Cancer Prognosis in India
The prognosis of the onset of cancer plays an inevitable role in saving the lives of the victims. The proposed "Machine Learning based Recommender System for Breast Cancer Prediction (MLRS-BC)" aims to provide an accurate recommendation for breast cancer prognosis through four distinct phases, namely: Data collection; Pre-processing; Training, Testing, Validation; and Prediction/Recommender.It is designed to predict the effect of risk factors associated with routine blood analysis in the Breast Cancer Coimbra Dataset (BCCD). The attributes of BCCD are age, body mass index, glucose, and insulin level in the blood, Homa, Leptin, Adiponectin, Resistin, and Monocitechemoattractant protein1. The Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) is used to evaluate the accuracy of the predictions. The MLRS-BC computes the error values for each attribute of BCCD. It recommends the best attribute having the least error rate as the pre-dominant attributes for breast cancer prognosis. It gains importance in automated breast cancer detection or classification, with a single optimal attribute, instead of engaging all the nine attributes of the dataset. MLRS-BC also recommends the best prediction algorithm for breast cancer detection. The outcomes of this research shall augment the quality of services in breast cancer care.