Lung Cancer Survivability Prediction based on Performance Using Classification Techniques of Support Vector Machines, C4.5 and Naive Bayes Algorithms for Healthcare Analytics
| Autor: | Pradeep K R, Naveen N C |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Computer science
medicine.medical_treatment Survivability 02 engineering and technology Disease Machine learning computer.software_genre 03 medical and health sciences Naive Bayes classifier 0302 clinical medicine 0202 electrical engineering electronic engineering information engineering medicine 030212 general & internal medicine Lung cancer General Environmental Science Chemotherapy Receiver operating characteristic business.industry Cancer medicine.disease Cancer treatment Support vector machine Data set General Earth and Planetary Sciences 020201 artificial intelligence & image processing Artificial intelligence business computer |
| Zdroj: | Procedia Computer Science. 132:412-420 |
| ISSN: | 1877-0509 |
| Popis: | The Healthcare Analytics(HcA) is a process in which clinical data is analyzed and patient’s treatment is performed. The treatment depends on the analysis of clinical data accumulated from Electronic Health Records (EHRs), pharmaceutical and research and development cost and claims of patient. Lung cancer is the most common among cancer disease and the foremost reason for deaths in both men and women. In this research work EHRs are analyzed and the survivability rate is predicted for lung cancer. Researchers apply Machine Learning Techniques (MLT)for predicting the survivability rate so that chemotherapy can be provided for cancer affected people. MLTare well accepted by doctors and work well in diagnosing and predicting cancer. An ensemble of Support Vector Machine (SVM), Naive Bayes (NBs)and classification trees (C4.5) can be used to evaluate patterns that are risk factors for lung cancer study. The North Central Cancer Treatment Group (NCCTG) lung cancer data set along with new patient data is used for evaluating the performance of support SVM, NBs and C4.5. The comparison isbased on accuracy, Area Under the Curve(AUC), Receiver Operating Characteristic (ROC) and the resultshows that C4.5 performs better in predicting lung cancer with the increase in training data set. |
| Databáze: | OpenAIRE |
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