Nursing Diagnosis of Urology Operating Room Based on New Association Classification Algorithm

Area of Science:

• Medical Informatics
• Data Mining
• Urology

Background:

• Medical engineering generates vast datasets crucial for disease diagnosis and research.
• Effective classification algorithms are vital for nursing diagnosis support systems, especially in urology.

Purpose of the Study:

• To analyze urological disease characteristics using medical data mining.
• To introduce and evaluate various classification algorithms for urological disease diagnosis.
• To propose a novel association classification algorithm (ACCF) for enhanced accuracy.

Main Methods:

• Analysis of urological disease characteristics via medical data mining.
• Comparative experiments using decision trees (ID3, C4.5), Bayesian classification, BP neural networks, and association rules.
• Development and implementation of a new association classification algorithm (ACCF) based on frequent closed item sets in C++.

Main Results:

• The proposed ACCF algorithm demonstrated superior performance compared to traditional methods.
• Experimental results confirmed improved accuracy in urological disease classification and prediction.
• The ACCF effectively addressed deficiencies in existing data mining algorithms.

Conclusions:

• The novel ACCF algorithm offers a significant advancement in urological disease classification.
• This approach enhances the accuracy and predictive power of diagnostic support systems.
• The findings support the integration of advanced data mining techniques in clinical practice.