Title: Data driven business strategies among healthcare entrepreneurs

Authors: Anjita Srivastava; Anubha Srivastava; Yashmita Awasthi; Aakanksha Uppal; Barkha Kakkar

Addresses: Department of Physics, Bundelkhand University, Jhansi, Uttar Pradesh, India ' Department of Management Studies, Institute of Technology and Science, Ghaziabad, Uttar Pradesh, India ' School of Commerce, Finance and Accountancy, Christ University, Bengaluru and Karnataka, India ' Symbiosis Law School, Noida, Constituent of Symbiosis International (Deemed) University, India ' Department of Information Technology, Institute of Technology and Science, Ghaziabad, Uttar Pradesh, India

Abstract: This paper aims to study various data-driven strategies among healthcare entrepreneurs to access foetal health, reduce maternal and infant mortality using data-driven strategies such as artificial intelligence. The purpose of this study is achieved by taking into account data comprising 2,126 records to classify foetal health into three categories: normal, suspect, and pathological, using tools such as decision tree, logistic regression. The study found that managers can use data-driven insights to drive digital transformation, tailor service offerings, and gain a competitive edge in a rapidly evolving healthcare ecosystem. Integrating predictive analytics into business operations not only enhances diagnostic accuracy but also improves resource allocation and operational efficiency. The findings of this study offer actionable insights on how healthcare practitioners can benefit from data-driven strategies. For healthcare entrepreneurs and decision-makers, this research underscores the strategic value of adopting AI-based technologies in service delivery.

Keywords: healthcare entrepreneurs; artificial intelligence; decision tree; logistic regression.

DOI: 10.1504/IJESB.2026.156153

International Journal of Entrepreneurship and Small Business, 2026 Vol.59 No.2, pp.141 - 161

Received: 02 Jul 2025
Accepted: 23 Jul 2025
Published online: 07 Sep 2026 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article