Real time data analysis and visualization for the breast cancer disease

Today, the amount of data that are digitally collected in the healthcare sector is tremendous and expanding rapidly, these data are inherently geospatial and temporal ranging from individual families to whole states and from minutes to decades. Therefore, they need sophisticated data management and analysis to be transformed into valuable knowledge. Healthcare professionals are faced with several challenges regarding extracting knowledge from this massive amount of data in order to support the decision-making process. To gain advantage of health care big data, big data analytics need to be exploited to utilize and understand patterns associations within these data thus make the right decision. In this research, an interactive data analysis and visualization tool is proposed to visually compare the performance of three machine learning algorithms on Wisconsin Diagnostic Breast Cancer (WDBC) dataset. The proposed model consists of two phases: input phase and analysis/visualization phase. It aims to allow the user to interactively compare the performance of three different ML algorithms (KNN, SVM and NB) in terms of accuracy, sensitivity and error rate in a user-friendly way. Here, SVM classifier has proven its efficiency and it is concluded as the best classifier with the highest accuracy as compared to the other two classifiers.