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Detecting Diseases in Medical Prescriptions Using Data Mining Tools and Combining Techniques.

Mehdi Teimouri1, Farshad Farzadfar2, Mahsa Soudi Alamdari1

  • 1Department of Network Science and Technology, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran. ; Non-communicable disease Research Center, Endocrinology and Metabolism Population Science Institute, Tehran University of Medical Sciences, Tehran, Iran.

Iranian Journal of Pharmaceutical Research : IJPR
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PubMed
Summary

This study used data mining to analyze outpatient prescriptions, identifying diseases with 95.32% accuracy using Support Vector Machine. This approach enhances disease prevalence estimation for community health assessment.

Keywords:
Data Mining; VotingDiagnosisMedical PrescriptionOutpatient DiseasesStackingWeighted Voting

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Area of Science:

  • Epidemiology
  • Data Science
  • Health Informatics

Background:

  • Disease prevalence data is crucial for understanding community health.
  • Outpatient prescriptions offer a rich source for epidemiological analysis.
  • Characterizing prescriptions can reveal disease patterns.

Purpose of the Study:

  • To calculate outpatient disease prevalence by analyzing prescription data.
  • To identify specific diseases from prescription records using data mining.
  • To evaluate and compare the performance of different data mining algorithms for this task.

Main Methods:

  • Collected and analyzed 1412 outpatient prescriptions.
  • Applied data mining techniques, including Support Vector Machine and Naïve Bayes.
  • Compared algorithm performance against a Naïve method and Nearest Neighbor.
  • Utilized combined methods to enhance classification accuracy.

Main Results:

  • Support Vector Machine achieved the highest accuracy at 95.32%.
  • The Naïve method showed lower performance with 67.71% accuracy.
  • Nearest Neighbor had the lowest accuracy among the tested classification algorithms.
  • Data mining algorithms demonstrated effective disease characterization from prescriptions.

Conclusions:

  • Data mining algorithms are highly effective for classifying outpatient diseases from prescription data.
  • The findings support the use of machine learning for large-scale epidemiological surveillance.
  • Accurate disease characterization from prescriptions can inform public health strategies.