Application of the joint clustering algorithm based on Gaussian kernels and differential privacy in lung cancer identification.

ABSTRACT: In the age of big data, privacy, particularly medical data privacy, is becoming increasingly important. Differential privacy (DP) has emerged as a key method for safeguarding privacy during data analysis and publishing. Cancer identification and classification play a vital role in early detection and treatment. This paper introduces a novel algorithm, DPFCM_GK, which combines differential privacy with fuzzy c-means (FCM) clustering using a Gaussian kernel function. The algorithm enhances cancer detection while ensuring data privacy. Three publicly available lung cancer datasets, along with a dataset from our hospital, are used to test and demonstrate the effectiveness of DPFCM_GK. The experimental results show that DPFCM_GK achieves high clustering accuracy and enhanced privacy as the priv

SUBMITTER: Yanping H 

PROVIDER: S-EPMC12084312 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature