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Applying data mining techniques to determine important parameters in chronic kidney disease and the relations of

Shahram Tahmasebian1, Marjan Ghazisaeedi1, Mostafa Langarizadeh2

  • 1Department of Health Information Management, School of Allied Medical Sciences, Tehran, University of Medical Sciences, Tehran, Iran.

Journal of Renal Injury Prevention
|May 13, 2017
PubMed

Insights

Data mining identified key factors influencing chronic kidney disease (CKD) progression in Iranian patients. Understanding these relationships aids in preventing or slowing the decline of kidney function.

Area of Science:

  • Nephrology
  • Data Science
  • Medical Informatics

Background:

  • Chronic kidney disease (CKD) is characterized by impaired kidney function and a progressive decline in glomerular filtration rate (GFR) over at least three months.
  • CKD can advance to end-stage kidney disease, necessitating interventions to slow its progression.
  • Patient medical records contain vast datasets valuable for identifying disease-influencing factors.

Purpose of the Study:

  • To utilize data mining techniques to identify critical parameters affecting CKD in Iranian patients.
  • To uncover the interrelationships between these identified parameters.

Main Methods:

  • A large dataset of 31,996 patients with CKD was compiled.
  • Data mining tools were employed to analyze the database for hidden patterns and associations.
  • The Cross Industry Standard Process for Data Mining (CRISP-DM) methodology was applied for data cleaning and analysis.

Main Results:

  • Data cleaning and mining algorithms successfully identified significant relationships between various parameters in CKD patients.
  • The analysis revealed key factors influencing the progression of chronic kidney disease within the study cohort.

Conclusions:

  • Data mining provides an effective approach to uncovering critical factors in chronic kidney disease.
  • Identifying these factors and their relationships can inform strategies for managing and potentially slowing CKD progression.

Keywords:
Association rulesChronic kidney diseaseData mining in medicineParameter importance

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