Breast Cancer Prediction Using Spark MLlib and ML Packages

Nowadays, Machine Learning has been applied in variety aspects of life especially in health care. Classifications using Machine learning has been greatly improved in order to make predictions and to support doctors making diagnoses. Furthermore, human lives are changing with Big Data covering a wide of array of science knowledge and with Data Mining solving problems by analyzing data and discovering patterns in present databases. The prediction process is heavily data driven and therefore advanced machine learning techniques are often utilized. In this paper, we will take a look at what types experiment data are typically used, do preliminary analysis on them, and generate breast cancer prediction models - all with PySpark and its machine learning frameworks. Using a database with more than a hundred sets of data gathered in routine blood analysis, the accuracy rates of detection and classification are about 72% and 83% respectively.