ArticleElectronic physician2017
Predictive model for survival in patients with gastric cancer.
Article in Electronic physician, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- A comparative study of explainable ensemble learning and logistic regression for predicting in-hospital mortality in the emergency department.Scientific reports · 2024Article
- Predicting Survival Among Colorectal Cancer Patients: Development and Validation of Polygenic Survival Score.Clinical and experimental gastroenterology · 2024Article
- Comparing In-Hospital Mortality Prediction by Senior Emergency Resident's Judgment and Prognostic Models in the Emergency Department.BioMed research international · 2023Article
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- Mortality risk factors in patients with gastric cancer using Bayesian and ordinary Lasso logistic models: a study in the Southeast of Iran.Gastroenterology and hepatology from bed to bench · 2020Article
- Expression of miR-141 and YAP1 in gastric carcinoma and modulation of cancer cell proliferation and apoptosis.International journal of clinical and experimental pathology · 2019Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
aimGastric cancer is one of the most prevalent cancers in the world. Characterized by poor prognosis, it is a frequent cause of cancer in Iran. The aim of the study was to design a predictive model of survival time for patients suffering from gastric cancer.
methodsThis was a historical cohort conducted between 2011 and 2016. Study population were 277 patients suffering from gastric cancer. Data were gathered from the Iranian Cancer Registry and the laboratory of Emam Reza Hospital in Mashhad, Iran. Patients or their relatives underwent interviews where it was needed. Missing values were imputed by data mining techniques. Fifteen factors were analyzed. Survival was addressed as a dependent variable. Then, the predictive model was designed by combining both genetic algorithm and logistic regression. Matlab 2014 software was used to combine them.
resultsOf the 277 patients, only survival of 80 patients was available whose data were used for designing the predictive model. Mean ?SD of missing values for each patient was 4.43?.41 combined predictive model achieved 72.57% accuracy. Sex, birth year, age at diagnosis time, age at diagnosis time of patients' family, family history of gastric cancer, and family history of other gastrointestinal cancers were six parameters associated with patient survival.
conclusionThe study revealed that imputing missing values by data mining techniques have a good accuracy. And it also revealed six parameters extracted by genetic algorithm effect on the survival of patients with gastric cancer. Our combined predictive model, with a good accuracy, is appropriate to forecast the survival of patients suffering from Gastric cancer. So, we suggest policy makers and specialists to apply it for prediction of patients' survival.
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