Unlocking the Future: Leveraging Big Data Analytics for Predictive Healthcare Insights

Authors

  • Protyusha Chatterjee CSE Department/student, JIS University, Kolkata, India
  • Akash Barman CSE Department/student, JIS University, Kolkata, India
  • Debashish Dey CSE Department/student, JIS University, Kolkata, India
  • Aditya Dutta CSE Department/student, JIS University, Kolkata, India
  • Radha Krishna Jana CSE Department/student, JIS University, Kolkata, India
  • Dharmpal singh CSE Department/student, JIS University, Kolkata, India
  • Sudipta Dutta CSE Department/student, JIS University, Kolkata, India
  • Nirbhay Mishra CSE Department/student, JIS University, Kolkata, India

Keywords:

Predictive analytics, big data analytics, Disease prediction, Disease prevention, Healthcare, Machine learning models, Medical data, Genetic data

Abstract

This article shows that predictive analytics using big data analytics has become a powerful tool for disease prediction and prevention in healthcare. This article provides an overview of the application of predictive analytics using big data analytics in healthcare. Machine learning models that use a wide variety of data, including medical data, genetic data, lifestyle data, and the environment, are used to identify and generate accurate predictions. Benefits of predictive testing in healthcare include early disease detection, personalised medicine, and lifestyle changes. Supports interventions that improve clinical outcomes. The allocation of resources and planning have also been simplified, and better treatment and prevention measures have been used. As a result, issues such as privacy concerns, data quality, and ethical considerations must be addressed. Predictive analytics from big data analytics has the potential to transform healthcare and improve patient care and public health outcomes.

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Published

2026-01-19

How to Cite

[1]
P. Chatterjee, “Unlocking the Future: Leveraging Big Data Analytics for Predictive Healthcare Insights”, Int. J. Comp. Sci. Eng., vol. 11, no. 1, pp. 114–119, Jan. 2026.

Issue

Vol. 11 No. 1 (2023): Special Issue-1 Nov Editon

Section

Research Article

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