Design of Decision Support System in the Metastatic Colorectal Cancer Data Set and Its Application
| Autor: | Tuba Pala, Ali Yilmaz Camurcu |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
Oncology
medicine.medical_specialty Decision support system Computer science Elektrik ve Elektronik Decision tree Mühendislik Machine learning computer.software_genre Naive Bayes classifier Engineering Internal medicine medicine Electrical and Electronic Engineering Cluster analysis Artificial neural network business.industry Building and Construction Support vector machine Medical Decision Support System Data Mining Classification K-Means Clustering Metastatic Colorectal Cancer Disease Data Set ComputingMethodologies_PATTERNRECOGNITION Multilayer perceptron Artificial intelligence Data pre-processing business computer |
| Zdroj: | Volume: 4, Issue: 1 12-16 Balkan Journal of Electrical and Computer Engineering |
| ISSN: | 2147-284X |
| Popis: | Developing the system which will help doctors with the result to be obtained from the medical data sets by realizing the design of the medical decision support system in which data mining methods are used is the primary objective of this study. The survivability condition of metastatic colorectal cancer disease has been predicted using data mining methods in the developed system. In the process of data mining, after the phase of data preprocessing, Support Vector Machines, Naive Bayes, Decision Trees, Artificial Neural Networks, Multilayer Perceptron, Logistic Regression algorithms have been used. In the study, two different medical decision support system models, classifying prediction and hybrid models, have been developed, and the results obtained from the two models have been compared after being examined. While the most successful algorithm is Support Vector Machines in Classifying Prediction Model in which only classification algorithms are applied, it is Decision Trees and Artificial Neural Networks which are the most successful algorithms with an accuracy rate of 100 per cent in Hybrid Prediction Model. In consequence of the classifying processes, when the accuracy rates of the models are examined, it is seen that while the accuracy rate of Classifying Prediction Model is 65-70%, this rate reaches 95-100% in Hybrid Prediction Model. It has been seen that accuracy rates have elicited very high and very close values for all of the algorithms with the realized hybrid structure, that is, with clustering, in the applications of the classifying algorithms combined. |
| Databáze: | OpenAIRE |
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