Predictive Analysis Techniques of Disease using Machine Learning

Electronic data have collected because of the health care area's boundless reception of PC based innovation. The primary goal of this essay is to discuss data mining's application to the provision of medical care. These data mining methods can also be applied in a number of examination and educational settings. The quickest arising area of clinical science is the Smart Health Prediction System. One area of computer science called data mining uses the information already available in the clinical field to estimate the event of diseases. We can learn from groups of large datasets by extracting new patterns using database management and machine learning tools. In this research paper, we examine different algorithmic data mining strategies that have been applied to the prediction of illness. Health organizations frequently use data mining to categorize diseases like diabetes and cancer in bioinformatics research. The review on how data mining methods are joined with machine learning to anticipate diseases in light of client side effects is made in the accompanying paper.

• Publication year

  2022

• Venue

  2022 Fourth International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT)

• Publication date

  2022-12-26

• Fields of study

  Not labeled

• Identifiers
  DOI 10.1109/ICERECT56837.2022.10060049
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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