A text mining approach to identify the relationship between gait-Parkinson's disease (PD) from PD based research articles

Recently the Parkinson's disease (PD) is getting a lot of attention because the economic impact of this disease to the society and to the country is huge. As the population of the old age people is increasing at a higher rate and will also increase at a higher rate in the future the early prediction as well as early diagnosis of the Parkinson's disease become a do or die situation for many developed countries. With the advent of the wearable device and some intelligent data mining approach the research related to Parkinson's disease has increasing at a higher rate throughout the world. As these disease affect most of the human body parts it is difficult for so many clinicians which part of the body should analyzed to get early detection of the disease. Lot of research has been done in different context related to Parkinson's disease a summarization of this topic using manual approach text long time. So to get the important information with the short time, in this paper a text mining based approach has been implemented to a group of texts related to PD that includes only abstracts of papers published in the last three years (2014–2017). We have used some visualization approach to highlight the relationship between gait-Parkinson's disease from the PD based research articles. This analysis will help the clinicians to judge the bonding between them and put focus on them based on the bonding.