Title: Discovering patterns of live birth occurrence before in vitro fertilisation treatment using association rule mining

Authors: Kamal Upreti; Divakar Singh; Anju Singh; Prashant Vats; Rishu Bhardwaj; Shreya Kapoor

Addresses: CHRIST (Deemed to be University), Delhi NCR, Ghaziabad, India ' UIT, Barkatullah University, Bhopal, Madhya Pradesh, India ' LNCT Group of Colleges, LNCT Campus Kalchuri Nagar, Raisen Road, Bhopal, India ' SCSE, Faculty of Engineering, Manipal University Jaipur, Jaipur, Rajasthan, India ' Chitkara Business School, Chitkara University, Rajpura, Punjab, India ' Dr. Akhilesh Das Gupta Institute of Professional Studies, Shastri Park, New Delhi, India

Abstract: According to estimates, in-vitro fertilisation (IVF) is credited for the delivery of over 9 million children globally, constituting it to be a highly remarkable as well as commercialised advanced healthcare treatment. Nonetheless, the majority of IVF treatments are now constrained by factors such as expense, access and most notably, labour-intensive, technically demanding processes carried out by qualified professionals. Advancement is thus crucial to maintaining the IVF market's rapid growth while also streamlining current procedures. This might also improve access, cost, and effectiveness while also managing therapeutic time efficiently and at a reasonable cost. IVF has become a renowned technique for addressing problems like endometriosis, poor embryo development, hereditary diseases of the parents, issues with the biological function, problems with counteracting agents that harm either eggs or sperm, the limited capacity of semen to penetrate cervical bodily fluid, and lower sperm count that lead to infertility in humans.

Keywords: in-vitro fertilisation; IVF; association rule mining; ARM; market basket analysis; donor; artificial intelligence; AI.

DOI: 10.1504/IJEH.2023.138250

International Journal of Electronic Healthcare, 2023 Vol.13 No.4, pp.275 - 294

Received: 11 Apr 2023
Accepted: 06 Oct 2023
Published online: 30 Apr 2024 *

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