Research on Medical Information Classification and Extraction Based on Optimized KNN Algorithm

Jingjing Hua; Mingtian Meng; Hanjia Qiu

doi:10.47297/wspbdsWSP2752-630520.20250502

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Research on Medical Information Classification and Extraction Based on Optimized KNN Algorithm

Volume 5, Issue 2

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Research on Medical Information Classification and Extraction Based on Optimized KNN Algorithm
Research on Medical Information Classification and Extraction Based on Optimized KNN Algorithm
Southwest University, Chongqing, 400700
Vol. 5, Issue 2, Pages: 113-118(2025)
Published：18 June 2025
DOI：10.47297/wspbdsWSP2752-630520.20250502
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Hua Jingjing,Meng Mingtian,Qiu Hanjia.Research on Medical Information Classification and Extraction Based on Optimized KNN Algorithm[J].Biomedical Data Science,2025,05(02):113-118.
DOI：

Hua Jingjing,Meng Mingtian,Qiu Hanjia.Research on Medical Information Classification and Extraction Based on Optimized KNN Algorithm[J].Biomedical Data Science,2025,05(02):113-118. DOI： 10.47297/wspbdsWSP2752-630520.20250502.

摘要

Abstract

The rapid advancement of big data technology poses challenges to medical information management

especially in terms of data diversity and structural complexity. This study focuses on the issue of missing values in medical laboratory test data and designs a missing value imputation scheme mainly based on the KNN algorithm. It uses weighted Euclidean distance to measure sample similarity and combines the features of neighboring samples to construct a dynamic filling model. Particularly

a feature weight adaptive adjustment strategy is introduced in the nearest neighbor selection mechanism

effectively enhancing the modeling effect of complex medical datasets. Seven standard medical datasets from the UCI database were selected for experiments

and the performance metrics of the KNN algorithm

improved weighted KNN model

random forest

support vector machine

and naive Bayes classifier were compared horizontally

[

]

. By improving feature engineering

the aim is to enhance the quality of medical data and optimize clinical decision support systems. These technological breakthroughs provide key support for the construction of intelligent medical data analysis platforms and are of great significanc

e for the advancement of precision medicine.

关键词

Keywords

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