Prediction of breast cancer recurrence using Classification Restricted Boltzmann Machine with Dropping
This work addresses breast cancer recurrence prediction, a critical healthcare issue, but is incremental as it builds on existing dropping methods with a minor generalization.
The paper tackles breast cancer recurrence prediction by applying a Classification Restricted Boltzmann Machine (ClassRBM) and introduces DropPart, a new dropping method using Beta distribution, achieving results on a real-life dataset of 949 cases.
In this paper, we apply Classification Restricted Boltzmann Machine (ClassRBM) to the problem of predicting breast cancer recurrence. According to the Polish National Cancer Registry, in 2010 only, the breast cancer caused almost 25% of all diagnosed cases of cancer in Poland. We propose how to use ClassRBM for predicting breast cancer return and discovering relevant inputs (symptoms) in illness reappearance. Next, we outline a general probabilistic framework for learning Boltzmann machines with masks, which we refer to as Dropping. The fashion of generating masks leads to different learning methods, i.e., DropOut, DropConnect. We propose a new method called DropPart which is a generalization of DropConnect. In DropPart the Beta distribution instead of Bernoulli distribution in DropConnect is used. At the end, we carry out an experiment using real-life dataset consisting of 949 cases, provided by the Institute of Oncology Ljubljana.