A Literature Review on Prediction of Chronic Diseases using Machine Learning Techniques

Main Article Content

Siddegowda C. J.
A. Jayanthila Devi


Background/ Purpose: Reviewing of the various work and literature in the proposed areas will help in developing a strong foundation of the domain on which the research is planned. The reason forth for the literature review is to become familiar in the health care domain. Since the area selected is the health care domain, the recent literature review is carried out as it is very important.

Objective: A strong background on health care domain is developed and a new problem which is not addressed is discussed. The gaps in the research area are identified. A new solution for solving the problem is designed and developed.

Design/Methodology: This work has adapted secondary source of data which is mainly journals, articles and review comments. The relevant literature is selected and a detailed study is conducted. This has helped in drafting the problem statement.

Findings/Results: The finding and drawbacks of all the recent work are well studied. The reason for the gap is also well studied and the results of each work are also well analyzed.

Research Limitations: A detailed study done on the chronic diseases and its impact has helped to open up the importance of studying about comorbid diseases. The limitations of various machine learning algorithms are also studied.

Originality/Value: This paper aims at studying the relevant existing literature that includes research journals, conference papers, technical book chapter and few web sources. All the papers selected were relevant to the proposed work and all papers are recent and from well reputed publisher.  The papers are cited by many authors.

Paper Type: Literature review paper is carried out on scientific papers, especially from well indexed services.


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How to Cite
Siddegowda C. J., & A. Jayanthila Devi. (2022). A Literature Review on Prediction of Chronic Diseases using Machine Learning Techniques. International Journal of Management, Technology and Social Sciences (IJMTS), 7(2), 28–49. https://doi.org/10.47992/IJMTS.2581.6012.0209