Type 2 diabetes mellitus associated pancreatic cancer prediction using combinations of machine learning models

Abstract

Background

Type 2 Diabetes Mellitus (T2DM) patients face an increased risk of developing pancreatic cancer (PaC), with studies reporting relative risk of 1.94 (confidence interval 1.66–2.27). Our goal was to identify multiple T2DM-PaC comorbidity genes and develop machine learning (ML) models for predicting T2DM-PaC comorbidity using transcriptomics gene expression datasets from blood.

Methods

Comorbidity genes from literature were extracted using Natural language processing. Using publicly available T2DM-PaC gene expression datasets we extracted differentially expressed genes, hub genes of co-expressed modules in weighted gene correlation network analysis, and highly perturbed genes from pathway simulations. We explored a wide range of ten ML algorithms spanning Linear Classifiers, Tree-Based Methods, Gradient Boosting Methods, and Naive Bayes Classifiers and different combinations of algorithm. For T2DM-PaC comorbidity prediction we constructed two different ML models one for T2DM and other for PaC, using T2DM-PaC comorbidity features.

Results

Sixty-seven T2DM-PaC comorbidity genes features were identified in total, among these ATM genes are already used in PaC diagnosis. In the T2DM model, the Logistic Regression Classifier-Support Vector Machine combination achieved an F1 score of 0.80 and Matthews Correlation Coefficient (MCC) of 0.65. In the PaC model, the Guassian Naive Bayes-eXtreme Gradient Boosting combination had an F1 score of 0.96 and MCC of 0.94. The T2DM-PaC ensemble model tested on a T2DM-PaC comorbidity dataset had an F1 score of 0.89 and MCC of 0.77.

Conclusion

Built ensemble ML models could identify T2DM-PaC comorbidity with 89 % accuracy. These ML models could aid in screening for PaC in T2DM patients and are available at https://github.com/suroseth/T2DM-PaC_comorbidity_predictor.