Title: Utilising predictive analytics for decision-making and improving healthcare services in public maternal healthcare database

Authors: Shelly Gupta; Shailendra Narayan Singh; Parsid Kumar Jain

Addresses: ASET, Amity University, Uttar Pradesh, India ' ASET, Amity University, Uttar Pradesh, India ' HCMS, Haryana, India

Abstract: Predictive analytics helps to improve the healthcare quality by supporting the healthcare planners in decision-making. Hence, in this paper, the predictive analytics enabled results on public maternal health data (2015-2016) of Uttar Pradesh state of India are discussed for enhancing the quality in public maternal healthcare. The major findings are that the districts with higher percentage of live births rate having weight less than 2.5 kg is an important parameter to be included during non-priority districts (NPDs) and priority districts (PDs) distribution. Also, more effort is needed towards the awareness of the deliveries to be done under trained skilled birth attendant (SBA) as the post natal care (PNC) checkups within 48 hours percentage are high when deliveries at home are taken under trained SBA. It is also analysed that the impact of sub-centres (SCs) availability is less to identify priority and non-priority districts.

Keywords: predictive analytics; public maternal health; machine learning; receiver operating characteristic; ROC; curve.

DOI: 10.1504/IJRIS.2021.114634

International Journal of Reasoning-based Intelligent Systems, 2021 Vol.13 No.2, pp.85 - 91

Received: 14 Sep 2019
Accepted: 30 Nov 2019

Published online: 30 Mar 2021 *

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