Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{153967,
        author = {Dr. Shameem Kappan and Rasheed N K},
        title = {A Survey on Various Tasks in Healthcare Predictive Analytics},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {9},
        pages = {644-664},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=153967},
        abstract = {Health care data related to a patient or community is very complex. As the health data is also rapidly increasing due to the increase in population and the technology which is capable of acquiring it, it is difficult for a healthcare professional like a doctor, epidemiologist, and policy makers to make good decisions based on this data. Thus, a healthcare professional faces a challenging task in predicting health- related decisions. However, advances in health care analytics using Machine e- learning predictive models have   solved   many real-world   problems. This is where a predictive model can be used to predict health-related outcomes by identifying patterns in healthcare data. As such models can assist healthcare professionals in good decision making which can reduce healthcare costs. For the last decade, machine learning scientists, statisticians, data scientists and healthcare professionals are collaboratively working on creating predicative models for complex diseases   and tasks.   This paper contains a comprehensive survey of 92 papers on various tasks involved in predictive healthcare analytics from 2012 onwards. A summary table for each task with classifier performances is also given. Hence, this paper will help new researchers who aim to enter the health care predictive analytics area.},
        keywords = {Epidemiologist, Data scientists, Policy makers, Healthcare analytics, Machine learning.},
        month = {},
        }