Data Mining Based Predictive Analysis Of Diabetic Diagnosis In Health Care: Overview

Authors

  • M. Shanmugavalli Research Scholar, Department of Mathematics, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Chennai, Tamilnadu
  • K. Sivakumar Professor, Department of Mathematics, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Chennai, Tamilnadu

Keywords:

Machine Learning, Data Mining, Diabetes Mellitus (DM), Diabetic complications, Disease prediction models

Abstract

The complexity of modernizing the healthcare industry's journey toward processing huge health data and accessing them for analysis and action will be considerably increased. Health research breakthroughs have resulted in a substantial quantity of data being generated from enormous electronic health records, high-throughput genomic data, for example as well as the collection of clinical information. The healthcare business confronts several obstacles, emphasizing the significance of data analytics development. In biosciences, machine learning and data mining methods are becoming more crucial in attempts to effectively convert all available data into valuable information. This is the goal of these approaches. mining techniques in today's medical studies. An overview is the goal of this work. The condition known as Diabetic Mellitus affects millions of individuals worldwide (DM). There were a lot of clinical datasets that were utilised. New hypotheses targeted at greater understanding and future study in DM may be derived from the title applications in the chosen works. According to the findings, this strategy is an effective method to treat and care for patients while also improving outcomes like as cost and availability.

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Published

01.10.2022

How to Cite

[1]
M. Shanmugavalli and K. . Sivakumar, “Data Mining Based Predictive Analysis Of Diabetic Diagnosis In Health Care: Overview”, Int J Intell Syst Appl Eng, vol. 10, no. 3, pp. 161–170, Oct. 2022.

Issue

Vol. 10 No. 3 (2022)

Section

Research Article

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