Analysis of Neurooncological Data to Predict Success of Operation Through Classification
| Autor: | Gul Tokdemir, Nergiz Ercil Cagiltay, Negin Bagherzadi, Alp Özgün Börcek, Hadi Hakan Maras |
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| Rok vydání: | 2016 |
| Předmět: |
Decision support system
Structured support vector machine Computer science business.industry Machine learning computer.software_genre Perceptron Relevance vector machine Support vector machine 03 medical and health sciences Naive Bayes classifier Statistical classification 0302 clinical medicine 030220 oncology & carcinogenesis 030211 gastroenterology & hepatology Personalized medicine Artificial intelligence Data mining business computer |
| Zdroj: | BCB |
| DOI: | 10.1145/2975167.2985645 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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