F-TBDA: A Frequency-Based Temporal Big Data Analytics Technique for Mining and Analyzing Quality-Of-Life Indicators of Cancer Patients

In this paper, we introduce and experimentally assess an innovative big data analytics technique for mining and analyzing Quality-of-Life Indicators (QoL) over time among patients with lung cancer and treated with immunotherapy. In more details, given datasets of QoL indicators collected over time, at regular intervals, the F-TBDA technique (Frequency-based Temporal Big Data Analytics) computes temporal relative frequency tables over fixed-time intervals where data of subsequent observations (i.e., intermediate therapy) are compared with the baseline observation (i.e., starting therapy). Then, on the basis of these relative frequency tables, both simple and complex frequency-based big data analytics tools are developed, in order to unveil hidden patterns over cancer patient therapies. Experimental results on top of a real-life dataset nicely complete the theoretical contributions we provide in our research.