Improving the Prediction of Survival in Cancer Patients by Using Machine Learning Techniques: Experience of Gene Expression Data: A Narrative Review

Today, despite the many advances in early detection of diseases, cancer patients have a poor prognosis and the survival rates in them are low. Recently, microarray technologies have been used for gathering thousands data about the gene expression level of cancer cells. These types of data are the ma...

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Bibliographic Details
Published in:Iranian journal of public health 2017-02, Vol.46 (2), p.165-172
Main Authors: Bashiri, Azadeh, Ghazisaeedi, Marjan, Safdari, Reza, Shahmoradi, Leila, Ehtesham, Hamide
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Cancer
Classification
Clinical decision support system
Clinical outcomes
Decision making
Decision support systems
Disease
Esophagus
Gene expression
Genomics
Machine-learning techniques
Medical prognosis
Medical research
Patients
Review
Studies
Survival
Survival analysis
Tumors
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Summary:Today, despite the many advances in early detection of diseases, cancer patients have a poor prognosis and the survival rates in them are low. Recently, microarray technologies have been used for gathering thousands data about the gene expression level of cancer cells. These types of data are the main indicators in survival prediction of cancer. This study highlights the improvement of survival prediction based on gene expression data by using machine learning techniques in cancer patients. This review article was conducted by searching articles between 2000 to 2016 in scientific databases and e-Journals. We used keywords such as machine learning, gene expression data, survival and cancer. Studies have shown the high accuracy and effectiveness of gene expression data in comparison with clinical data in survival prediction. Because of bewildering and high volume of such data, studies have highlighted the importance of machine learning algorithms such as Artificial Neural Networks (ANN) to find out the distinctive signatures of gene expression in cancer patients. These algorithms improve the efficiency of probing and analyzing gene expression in cancer profiles for survival prediction of cancer. By attention to the capabilities of machine learning techniques in proteomics and genomics applications, developing clinical decision support systems based on these methods for analyzing gene expression data can prevent potential errors in survival estimation, provide appropriate and individualized treatments to patients and improve the prognosis of cancers.
ISSN:2251-6085
2251-6093