Assessing the interrelationship between different breast cancer diagnostic parameters and their overall effects on patient survival using machine learning tools
Machine learning has been widely used in predicting the survival of cancer patients. This information is useful for patient planning and medical management. In this study, we aim to assess the interrelationship between various clinical diagnostic parameters for breast cancer such as type of surgery, lymph node status, cancer stage, tumour location, histopathological grade, size, receptor status and tumour markers and to evaluate how they relate with breast cancer patient survival and mortality. Various machine learning algorithms will be modelled and their predictive accuracy compared.
Identify how various diagnostic parameters relation with each other.
Identify the effects of the above parameters on survival and mortality rate.
Compare the performance of various algorithms on survival prediction.
Modell building on survival and testing the performance of the developed modells.
no external collaborators