According to the United Nations Sustainable Development Goal No. 3 (SDG-Goal 3), for sustainable development it is imperative to ensure health and well-being across all ages, and is achievable only through effective and continuous healthcare monitoring. But in India and other third world countries, healthcare monitoring is poor compared to other countries in the world, in spite of it being affordable. The global healthcare smart wearable healthcare (SWH) devices market is expected to rise up at a CAGR (Compound Annual Growth Rate) of 5.6% and by 2020 it is expected to reach 25 Billion (GVR Report, 2016). The growing incidences of lifestyle diseases, sedentary lifestyle, busy work schedules, technological advancements in healthcare monitoring devices, and increased usage of remote devicesseems to be some of the important factors fuelling this growth.

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Purpose -The aim of this work is to increase awareness of the potential of the technique of text mining to discover knowledge and further promote research collaboration between knowledge management and the information technology communities. Since its emergence, text mining has involved multidisciplinary studies, focused primarily on database technology, Web-based collaborative writing, text analysis, machine learning and knowledge discovery. However, owing to the large amount of research in this field, it is becoming increasingly difficult to identify existing studies and therefore suggest new topics. Design/methodology/approach -This article offers a systematic review of 85 academic outputs (articles and books) focused on knowledge discovery derived from the text mining technique. The systematic review is conducted by applying ''text mining at the term level, in which knowledge discovery takes place on a more focused collection of words and phrases that are extracted from and label each document'' (Feldman et al., 1998, p. 1). Findings -The results revealed that the keywords extracted to be associated with the main labels, id est, knowledge discovery and text mining, can be categorized in two periods: from 1998 to 2009, the term knowledge and text were always used. From 2010 to 2017 in addition to these terms, sentiment analysis, review manipulation, microblogging data and knowledgeable users were the other terms frequently used. Besides this, it is possible to notice the technical, engineering nature of each term present in the first decade. Whereas, a diverse range of fields such as business, marketing and finance emerged from 2010 to 2017 owing to a greater interest in the online environment. Originality/value -This is a first comprehensive systematic review on knowledge discovery and text mining through the use of a text mining technique at term level, which offers to reduce redundant research and to avoid the possibility of missing relevant publications.

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