PREDICTION OF RECURRENCE AND MORTALITY OF ORAL TONGUE CANCER USING ARTIFICIAL NEURAL NETWORK (A case study of 5 hospitals in Finland and 1 hospital from Sao Paulo, Brazil)
Alabi, Rasheed Omobolaji (2017)
Alabi, Rasheed Omobolaji
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Cancer is a dreadful disease that had caused the death of millions of people. It is characterized by an uncontrollable growth of cell to form lumps or masses of tissue that are known as tumour. Therefore, it is a concern to all and sundry as these tumours mostly release hormones which have negative impact on the body system. Data mining approaches, statistical methods and machine learning algorithms have been proposed for effective cancer data classification. Artificial Neural Networks (ANN) have been used in this thesis for the prediction of recurrence and mortality of oral tongue cancer in patients. Similarly, ANN was also used to examine the diagnostic and prognostic factors. This was aimed at determining which of these diagnostic and prognostics factors had influence on the prediction of recurrence and mortality of oral tongue cancer in patients. Three different ANN have been applied for the learning and testing phases. The aim was to find the most effective technique. They are Elman, Feedforward, and Layer Recurrent neural networks techniques. Elman neural network was not able to make acceptable prediction of the recurrence or the mortality of tongue cancer based on the data. In contrast, Feedforward neural network captured the relationship between the prognostic factors and correctly predicted recurrence. However, it failed to predict the mortality based on the patient's data. Layer Recurrence neural network has been very effective and successfully predicted the recurrence and the mortality of oral tongue cancer in patients. The constructed layered recurrence neural network has been used to investigate the correlation between the prognostic factors. It was found that out of 11 prognostic factors in the data sheet, it was only 5 of them that had considerable impact on the recurrence and mortality. These are grade, depth, budding, modified stage, and gender. Time in months and disease free months were also used to train the network.
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