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A survey on data mining techniques used in medicine
Saba Maleki Birjandi1, Seyed Hossein Khasteh1,2
1School of Computer Engineering, K. N. Toosi University of Technology, 16317-14191 Tehran, Iran.
Journal of Diabetes and Metabolic Disorders
|December 13, 2021
Summary
This study overviews data mining applications in medicine, detailing techniques like Decision Trees and Random Forests for disease diagnosis and prediction. It compares various methods to guide future medical data mining research.
Area of Science:
- Medical Informatics
- Computer Science
Background:
- Data mining analyzes large datasets to find patterns for prediction and decision-making.
- Applications span marketing, banking, and medicine, with a focus here on healthcare.
Discussion:
- This paper reviews data mining applications in medicine, highlighting challenges and prior research.
- It examines techniques like Decision Tree, Random Forest, K-means Clustering, Support Vector Machine, Logistic Regression, Neural Network, Naive Bayes, and association rule mining.
Key Insights:
- Data mining techniques are employed for disease diagnosis, prognosis, classification, predictive modeling, and risk factor analysis.
- The study compares the efficacy of different data mining methods in medical contexts.
Outlook:
- Provides a clear overview for researchers entering the field of medical data mining.
- Facilitates informed selection of data mining techniques for specific medical applications.
Keywords:
Association rulesData miningDecision treeLinear regressionMedical data miningStatistical methodsMore Related Videos
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