Single and multiple time–point artificial neural networks models for predicting the survival of gastric cancer patients
The extensive availability of recent computational models and data mining techniques for data analysis calls for researchers and practitioners in the medical field to opt for the most suitable strategies to confront clinical prediction problems. In many clinical research work, the main outcome...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2016
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| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/67515/1/IPM%202016%2015%20IR.pdf |
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| Summary: | The extensive availability of recent computational models and data mining techniques
for data analysis calls for researchers and practitioners in the medical field to opt for the
most suitable strategies to confront clinical prediction problems. In many clinical
research work, the main outcome under investigation is the time until an event occurs.
Survival models are a collection of statistical procedures used to analyse data where the
time until an event is of interest. Particularly the application of a data mining method
known as ‘neural networks’ offers methodological and technical solutions to the
problems of survival data analysis and prognostic model development.
In this context, artificial neural networks (ANN) have some advantages over
conventional statistical tools, especially in the presence of complex prognostic
relationships. ANN model applications for modeling the survival of gastric cancer
patients have been highlighted in a number of studies but without a full account of
censored survival data. The primary task under investigation in this thesis is to develop
neural network methodologies for modeling gastric cancer survivability and fill the gap
in the current literature by adopting strategies that directly incorporate censored
observations in the process of constructing a neural network model. The dataset used in
the study comprises of patients with confirmed gastric cancer who underwent surgery at
the Cancer Registry Center of Taleghani Hospital, Tehran, Iran.
To achieve the research aims, single and multiple time-point ANN models are proposed.
The first model is a single time-point ANN designed to predict the survival of patients at
specific time points. The second is a multiple time-point model specifically designed to
provide individualized survival predictions at different time points. Thus, an individual
survival curve can be generated for a particular patient by plotting the survival
probabilities produced by output units, which render the system more useful in clinical
settings. The third model is a softmax ANN designed to estimate the unconditional probability of death and predict the time period during which death is likely to occur for
an individual patient. All models are extended to incorporate censored data. Employing
the strategies for imputing the eventual outcome for censored patients has allowed all the
available data to be used in developing an ANN predictor model. Several criteria are
employed to validate the models. The research demonstrated how ANNs can be used in
the survival analysis for predictive purposes without imposing any restricting
assumptions. The proposed models provide accurate predictions of survival with high
levels of sensitivity and specificity. Additionally, the sensitivity analysis provided
information about the relative importance of each input variable in predicting the
outcome. To sum up,
The ANN survival models presented in this thesis provide a framework for modelling
survival data with censorship and facilitate individualized survival predictions. The
findings will provide physicians and medical practitioners with information to improve
gastric cancer prognosis and may assist in the selection of appropriate treatment plans
for individual patients as well as efficient follow-up planning. |
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