Prediction Performance of Deep Learning for Colon Cancer Survival Prediction on SEER Data

Colon and rectal cancers are the most common kinds of cancer globally. Colon cancer is more prevalent in men than in women. Early detection increases the likelihood of survival, and treatment significantly increases the likelihood of eradicating the disease. The Surveillance, Epidemiology, and End R...

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Bibliographic Details
Published in:BioMed research international 2022-06, Vol.2022, p.1467070-12
Main Authors: Gupta, Surbhi, Kalaivani, S., Rajasundaram, Archana, Ameta, Gaurav Kumar, Oleiwi, Ahmed Kareem, Dugbakie, Betty Nokobi
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Artificial intelligence
Biomarkers
Cancer
Cancer therapies
Colon
Colon cancer
Colonic Neoplasms - epidemiology
Colorectal cancer
Connective tissue
Deep Learning
Diagnosis
Disease
Endoscopy
Epidemiology
Event history analysis
Female
Geographical locations
Humans
Lymphatic system
Machine learning
Male
Medical prognosis
Medical research
Metastasis
Methods
Performance prediction
Physicians
Population statistics
Prediction models
Prognosis
ROC Curve
SEER Program
Statistical analysis
Statistics
Stomach
Survival
United States - epidemiology
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Summary:Colon and rectal cancers are the most common kinds of cancer globally. Colon cancer is more prevalent in men than in women. Early detection increases the likelihood of survival, and treatment significantly increases the likelihood of eradicating the disease. The Surveillance, Epidemiology, and End Results (SEER) programme is an excellent source of domestic cancer statistics. SEER includes nearly 30% of the United States population, covering various races and geographic locations. The data are made public via the SEER website when a SEER limited-use data agreement form is submitted and approved. We investigate data from the SEER programme, specifically colon cancer statistics, in this study. Our objective is to create reliable colon cancer survival and conditional survival prediction algorithms. In this study, we have presented an overview of cancer diagnosis methods and the treatments used to cure cancer. This paper presents an analysis of prediction performance of multiple deep learning approaches. The performance of multiple deep learning models is thoroughly examined to discover which algorithm surpasses the others, followed by an investigation of the network’s prediction accuracy. The simulation outcomes indicate that automated prediction models can predict colon cancer patient survival. Deep autoencoders displayed the best performance outcomes attaining 97% accuracy and 95% area under curve-receiver operating characteristic (AUC-ROC).
ISSN:2314-6133
2314-6141
DOI:10.1155/2022/1467070