Prediction of gastrointestinal cancers in the ONCONUT cohort study: comparison between logistic regression and artificial neural network

Rossella Donghia; Vito Guerra; Giovanni Misciagna; Carmine Loiacono; Antonio Brunetti; Vitoantonio Bevilacqua

doi:10.3389/fonc.2023.1110999

Prediction of gastrointestinal cancers in the ONCONUT cohort study: comparison between logistic regression and artificial neural network

Front Oncol. 2023 Apr 24:13:1110999. doi: 10.3389/fonc.2023.1110999. eCollection 2023.

Authors

Rossella Donghia¹, Vito Guerra¹, Giovanni Misciagna², Carmine Loiacono³, Antonio Brunetti³, Vitoantonio Bevilacqua³

Affiliations

¹ Data Science, National Institute of Gastroenterology - IRCCS "Saverio de Bellis", Castellana Grotte (BA), Italy.
² Scientific and Ethical Committee Polyclinic Hospital, University of Bari, Bari, Italy.
³ Department of Electrical and Information Engineering, Polytechnic University of Bari, Bari, Italy.

Abstract

Background: Artificial neural networks (ANNs) and logistic regression (LR) are the models of chosen in many medical data classification tasks. Several published articles were based on summarizing the differences and similarities of these models from a technical point of view and critically assessing the quality of the models. The aim of this study was to compare ANN and LR the statistical techniques to predict gastrointestinal cancer in an elderly cohort in Southern Italy (ONCONUT study).

Method: In 1992, ONCONUT was started with the aim of evaluating the relationship between diet and cancer development in a Southern Italian elderly population. Patients with gastrointestinal cancer (ICD-10 from 150.0 to 159.9) were included in the study (n = 3,545).

Results: This cohort was used to train and test the ANN and LR. LR was evaluated separately for macro- and micronutrients, and the accuracy was evaluated based on true positives and true negatives versus the total (97.15%). Then, ANN was trained and the accuracy was evaluated (96.61% for macronutrients and 97.06% for micronutrients). To further investigate the classification capabilities of ANN, k-fold cross-validation and genetic algorithm (GA) were used after balancing the dataset among classes.

Conclusions: Both LR and ANN had high accuracy and similar performance. Both models had the potential to be used as decision clinical support integrated into clinical practice, because in many circumstances, the use of a simple LR model was likely to be adequate for real-world needs, but in others in which there were large amounts of data, the application of advanced analytic tools such as ANNs could be indicated, and the GA optimizer needed to optimize the accuracy of ANN.

Keywords: artificial neural network; gastrointestinal cancer; logistic regression; machine learning; nutrition.

Grants and funding

This study was supported by the Italian Ministry of Health with the “Ricerca Corrente 2023” Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) “S. de Bellis,” funded by research projects.