Data Handling Algorithms for the Healthcare System for the Prediction of Diabetes in Health Data Science (HDS): A Review Report

Year : 2024 | Volume : 11 | Issue : 02 | Page : 1 10
    By

    Vinay Bhatt,

  • Mayank Kumar,

  1. Research Scholar, Department of Computer Science and Engineering, Asian International University, Imphal West, Manipur, India
  2. Associate professor, Department of Computer Science and Engineering, Asian International University, Imphal West, Manipur, India

Abstract

In recent years, diabetes has become the biggest disease in different countries around the world. This disease is caused by adulteration in food ingredients, unhealthy food habits, a lack of physical exercise, and changing the lifestyle every time without a routine chart. The main objective of this review paper is to provide a proper understanding of the machine learning algorithm used in the healthcare system to handle diabetic patients’ data. We use a previous research article for the idea of machine learning (ML) algorithm implementation in diabetic prediction to find more accuracy. The handling of diabetes patients’ data is very difficult in the healthcare system, so use the modern technology of computer science as data science. Data science technology is used for handling data in healthcare systems, so the term is introduced as health data science (HDS). In this survey paper, we review different data handling algorithms as ML algorithms for handling diabetic patient data. This paper presents diabetes prediction based on previous research, which is discussed in the literature review section of this paper. In previous work, different ML algorithms such as Support vector machine (SVM), K-nearest neighbor (KNN), Logistic regression (LR), Random forest (RF), Decision tree (DT), Deep neural network (DNN), and Naïve Bayesian classifiers were used for handling the diabetes data, but different challenges faced researchers, so we focused on the challenges of previous research works. In this review paper, we focus on the research challenges of previous research and set research goals on behalf of research gaps for the next research directions. The future work of this paper is to analyze different data handling algorithms for the prediction of diabetes in different cases.

Keywords: Data science, health data science (HDS) machine learning (ML), supervised learning algorithm, data handling algorithms, Support vector machine (SVM), K-nearest neighbor (KNN), Random forest (RF), Decision tree (DT), DNN, healthcare system, diabetic prediction

[This article belongs to Journal of Artificial Intelligence Research & Advances ]

How to cite this article:
Vinay Bhatt, Mayank Kumar. Data Handling Algorithms for the Healthcare System for the Prediction of Diabetes in Health Data Science (HDS): A Review Report. Journal of Artificial Intelligence Research & Advances. 2024; 11(02):1-10.
How to cite this URL:
Vinay Bhatt, Mayank Kumar. Data Handling Algorithms for the Healthcare System for the Prediction of Diabetes in Health Data Science (HDS): A Review Report. Journal of Artificial Intelligence Research & Advances. 2024; 11(02):1-10. Available from: https://journals.stmjournals.com/joaira/article=2024/view=155828


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Regular Issue Subscription Review Article
Volume 11
Issue 02
Received 31/01/2024
Accepted 06/02/2024
Published 20/06/2024
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