hrishitelang/Breast-Cancer-Prediction

Cancer is a collection of related diseases, in which some of the body’s cells begin to divide without stopping and spread into surrounding tissues. Regardless of the view of cancer may be, it is exaggerated and over-generalized. While a diagnosis of cancer may still leave patients feeling helpless and out of control, in many cases today there is cause for hope rather than a blinkered vision of survival. The basic aim of our project is to ensure that patients with a risk or borderline edge of getting cancer shall get themselves digitally scanned, that would eventually generate a report. This report shall achieve in alluding convoluted details regarding certain possible properties of tumours that could be sent for prediction so that they could immediately diagnose it if at all it is predicted to be malignant. The importance of classifying cancer patients into high or low-risk groups has led many research teams, from the biomedical and the bioinformatics field, to study the application of machine learning (ML) methods. Therefore, these techniques have been utilized as an aim to model the progression and treatment of cancerous conditions. In addition, the ability of ML tools to detect key features from complex datasets reveals their importance. Support vector machine has become an increasingly popular tool for machine learning tasks involving classification, regression or novelty detection. Training a support vector machine requires the solution of a very large quadratic programming problem. Up to now, several approaches exist for circumventing the above shortcomings and work well with the dataset. And besides, till now the project has confined its attempt to diagnose breast cancer only. In this way, we can affirm that the prognosis of cancer can be achieved, and accordingly, we can produce outputs for the same.

Jupyter Notebook