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COVID-19 patient diagnosis and treatment data mining algorithm based on association rules.

Zicheng Shan1, Wei Miao1

  • 1Artificial Intelligence Research Institute Donghua University Shanghai China.

Expert Systems
|December 13, 2021
PubMed
Summary

This study uses association rules for COVID-19 data mining to analyze patient diagnosis, treatment, and drug efficacy. Findings offer insights for rational drug use and understanding COVID-19 complications.

Keywords:
COVID‐19 patientsassociation rulesdata warehousediagnosis treatment data miningonline analytical processing

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Area of Science:

  • Data Mining
  • Bioinformatics
  • Medical Informatics

Background:

  • Association rules are vital in data mining for applications like web mining and bioinformatics.
  • COVID-19 patient data mining presents challenges in processing and analyzing complex medical information.

Purpose of the Study:

  • To develop and apply an association rule-based data mining algorithm for COVID-19 patient diagnosis and treatment.
  • To analyze the efficacy of drug treatments and identify patterns in COVID-19 complications.

Main Methods:

  • Data preprocessing and classification of disease types were performed to improve data mining efficiency.
  • Association rule mining was applied to analyze key time intervals, causes of death, and drug usage frequency.
  • A knowledge base was constructed for decision support, enabling prediction through a display interface.

Main Results:

  • The study identified associations between COVID-19, Coronary Heart Disease (CHD), and hypertension, with 46.5% of COVID-19 and CHD patients likely to have hypertension.
  • Analysis provided insights into the effectiveness of drug treatments for COVID-19 patients.
  • The data mining approach facilitated the extraction of association rules for COVID-19 complications.

Conclusions:

  • The developed association rule mining algorithm can effectively analyze COVID-19 patient data.
  • Results provide a reference for rational drug use and understanding disease progression and complications in COVID-19.
  • The study highlights the potential of data mining in medical informatics for clinical decision support.