Modeling Epidemiology Data with Machine Learning Technique to Detect Risk Factors for Gastric Cancer.

Purpose: Gastric cancer (GC) ranks as the 7th most common cancer worldwide and a leading cause of cancer mortality. In Iran, stomach malignancies are the most common fatal cancers with higher than world average incidence. In recent years, methods like machine learning that provide the opportunity of merging health issues with computational power and learning capacity have caught considerable attention for prediction and diagnosis of diseases.

Investigating heavy-metal soil contamination state on the rate of stomach cancer using remote sensing spectral features.

Heavy metal (HM) contamination in agricultural soils has been a serious environmental and health problem in the past decades. High concentration of HM threatens human health and can be a risk factor for many diseases such as stomach cancer. In order to investigate the relationship between HM content and stomach cancer, the under-study area should be adequately large so that the possible relationship between soil contamination and the patients' distribution can be studied.