Predicting breast cancer progression by using cell-free DNA

Thesis Submitted to the Faculty of Information in partial fulfillment of the requirements for the award of Master of Science in information TechnologyCancer is among the leading causes of deaths in Kenya after infectious and cardiovascular diseases. Among the various forms of cancer, breast cancer accounts for a significant percentage of all new cancer incidences in the country and has a high mortality rate. On a global level, breast cancer is considered the most common cancer. Treatment methods employed vary from patient to patient due to factors such as the stage, age, and health. Treatment methods such as surgery, radiotherapy, chemotherapy or a combination of all have been used all to varying degrees of success and are not always efficient. However, these modalities have been employed successfully when the disease is detected early. This research applied deep neural networks coupled with genetic algorithms to build a learning model that evaluated the biomarkers obtained from cell-free DNA. The model was able to predict progression of breast cancer. The research, in addition, employed an agile, data-driven methodology due to its recursive nature producing a model with a higher degree of accuracy and specificity. The model developed was able to attain an accuracy of 94% in predicting breast cancer progression