Machine Learning Algorithms for Predicting Breast Cancer

Breast cancer has become one of the common factors these days. Although the fact is that most of the well known general hospitals but not all general hospitals have the facility to diagnose breast cancer through mammograms. Waiting for a long time so that the breast cancer gets diagnosed, it might be too late increasing the possibility of cancer to spread. Therefore a computerised breast cancer diagnosis has been developed to reduce the time taken to diagnose the breast cancer and reduce the death rate. One of the techniques to detect breast cancer is using a variety of machine learning algorithms and methods for more accurate predicting of cancer.

Breast cancer is one of the most common diseases among women that leads to death. Breast cancer can be diagnosed by classifying tumours. There are two different types of tumours such as malignant and benign tumours. Physicians need a reliable diagnostic procedure to distinguish between these tumours. But generally, it is very difficult to distinguish tumours even by the experts. Hence automation of diagnostic system is needed for diagnosing tumours. After attempting many researchers it has now been proven that these algorithms work better in detecting cancer diagnosis apart from detecting the survivability of cancers in human beings.

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning basically focuses more on the development of computer programs that can access data and use it learn for themselves.

The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The main goal and objective are to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.

1. Supervised learning.  
2. Unsupervised learning.  
3. Semi-supervised learning.
4. Reinforcement learning.  
5. Transduction.
6. Learning to learn.

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