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Establishing Machine Learning Models to Predict the Early Risk of Gastric Cancer Based on Lifestyle Factors by Mohammad Reza Afrash; Mohsen Shafiee; Hadi Kazemi-Arpanahi is a Medicine article available to read on EtoBox.

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## Abstract ## Background Gastric cancer is one of the leading causes of death worldwide. Screening for gastric cancer greatly relies on endoscopy and pathology biopsy, which are invasive and pose financial burdens. Thus, the prevention of the disease by modifying lifestyle-related behaviors and dietary habits or even the prevention of risk factor formation is of great importance. This study aimed to construct an inexpensive, non-invasive, fast, and high-precision diagnostic model using six machine learning (ML) algorithms to classify patients at high or low risk of developing gastric cancer by analyzing individual lifestyle factors. ## Methods This retrospective study used the data of 2029 individuals from the gastric cancer database of Ayatollah Taleghani Hospital in Abadan City, Iran. The data were randomly separated into training and test sets (ratio 0.7:0.3). Six ML methods, including multilayer perceptron (MLP), support vector machine (SVM) (linear kernel), SVM (RBF kernel), k-nearest neighbors (KNN) (K = 1, 3, 7, 9), random forest (RF), and eXtreme Gradient Boosting (XGBoost), were trained to construct prognostic models before and after performing the relief feature selecti

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Author

Mohammad Reza Afrash; Mohsen Shafiee; Hadi Kazemi-Arpanahi

Publisher

Springer Science and Business Media LLC

Published

2023

Language

EN

Field

Medicine (Health Sciences)