Classification based on clustering model for predicting main outcomes of breast cancer using Hyper-Parameters Optimization

Abstract

Breast cancer is a deadly disease in women. Predicting the breast cancer outcomes is very useful in determining the efficient treatment plan for the new breast cancer patients. Predicting the breast cancer outcomes (also called Prognosis) are done based on the previous patient's data, which show the patient's characteristics and how the doctors treated the patient. In this paper we propose a new efficient model for predicting the main outcomes; Survival Rate, Disease Free Survival, and Recurrence detection; of breast cancer. The proposed model utilizes two techniques to increase the accuracy of the predictive results. The first technique is applying the classification model on various data clusters rather than the full dataset. In such steps, the data is grouped in different clusters according to the similarity of the main characteristics, then the classification model is applied on these clusters. The second technique is using the Hyper-Parameters Optimization (also called Hyper-Parameters Tuning) to increase the accuracy of the classification model. In this step, the proposed model uses Hyper-Parameters Optimization to find a tuple of hyper-parameters that yields on the optimal model which minimizes a predefined loss function on given dataset. The experimental study shows in detail how utilizing such two techniques results in an efficient prediction model producing accurate results.