ANALYSIS OF NEUROONCOLOGICAL DATA TO PREDICT SUCCESS OF OPERATION THROUGH CLASSIFICATION

Date

2016-10-05

Author

Bagherzadi, Negin
BÖRCEK, ALP ÖZGÜN
TOKDEMİR, GÜL
ÇAĞILTAY, NERGİZ
MARAŞ, HADİ HAKAN

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Data mining algorithms have been applied in various fields of medicine to get insights about diagnosis and treatment of certain diseases. This gives rise to more research on personalized medicine as patient data can be utilized to predict outcomes of certain treatment procedures. Accordingly, this study aims to create a model to provide decision support for surgeons in Neurooncology surgery. For this purpose, we have analyzed clinical pathology records of Neurooncology patients through various classification algorithms, namely Support Vector Machine, Multi Perceptron and Naive Bayes methods, and compared their performances with the aim of predicting surgery complication. A large number of factors have been considered to classify and predict percentage of patient's complication in surgery. Some of the factors found to be predictive were age, sex, clinical presentation, previous surgery type etc. For classification models built up using Support Vector Machine, Naive Bayes and Multi Perceptron, Classification trials for Support Vector Machine have shown %77.47 generalization accuracy, which was established by 5-fold cross-validation.

Subject Keywords

Data Mining, Neuroocology, Classifier, Multi Perceptron, Naive Bayes, Support Vector Machine

URI

https://hdl.handle.net/11511/68171

Collections

Graduate School of Natural and Applied Sciences, Conference / Seminar