Data privacy-aware machine learning approach in pancreatic cancer diagnosis

Abstract

Problem Pancreatic ductal adenocarcinoma (PDAC) is considered a highly lethal cancer due to its advanced stage diagnosis. The fve-year survival rate after diagnosis is less than 10%. However, if diagnosed early, the fve-year survival rate can reach up to 70%. Early diagnosis of PDAC can aid treatment and improve survival rates by taking necessary precautions. The challenge is to develop a reliable, data privacy-aware machine learning approach that can accurately diagnose pancreatic cancer with biomarkers. Aim The study aims to diagnose a patient’s pancreatic cancer while ensuring the confdentiality of patient records. In addition, the study aims to guide researchers and clinicians in developing innovative methods for diagnosing pancreatic cancer. Methods Machine learning, a branch of artifcial intelligence, can identify patterns by analyzing large datasets. The study pre-processed a dataset containing urine biomarkers with operations such as flling in missing values, cleaning outliers, and feature selection. The data was encrypted using the Fernet encryption algorithm to ensure confdentiality. Ten separate machine learning models were applied to predict individuals with PDAC. Performance metrics such as F1 score, recall, precision, and accuracy were used in the modeling process. Results Among the 590 clinical records analyzed, 199 (33.7%) belonged to patients with pancreatic cancer, 208 (35.3%) to patients with non-cancerous pancreatic disorders (such as benign hepatobiliary disease), and 183 (31%) to healthy individuals. The LGBM algorithm showed the highest efciency by achieving an accuracy of 98.8%. The accuracy of the other algorithms ranged from 98 to 86%. In order to understand which features are more critical and which data the model is based on, the analysis found that the features “plasma_CA19_9”, REG1A, TFF1, and LYVE1 have high importance levels. The LIME analysis also analyzed which features of the model are important in the decision-making process. Conclusions This research outlines a data privacy-aware machine learning tool for predicting PDAC. The results show that a promising approach can be presented for clinical application. Future research should expand the dataset and focus on validation by applying it to various populations.