Name
Title | ||
|---|---|---|
Application of Machine Learning Techniques for Prediction of Radiation Pneumonitis in Lung Cancer Patients |
Abstract | ||
|---|---|---|
Lung cancer patients who receive radiotherapy as part of their treatment are at risk radiation-induced lung injury known as radiation pneumonitis (RP). RP is a potentially fatal side effect to treatment. Hence, new methods are needed to guide physicians to prescribe targeted therapy dosage to patients at high risk of RP. Several predictive models based on traditional statistical methods and machine learning techniques have been reported, however, no guidance to variation in performance has not been provided to date. Therefore, in this study, we compare several widely used classification algorithms in the machine learning field are used to distinguish between different risk groups of RP. The performance of these classification algorithms is evaluated in conjunction with several feature selection strategy and the impact of the feature selection on performance is further evaluated. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ICMLA.2009.118 | ICMLA |
Keywords | Field | DocType |
radiation pneumonitis,lung cancer,radiation therapy,high risk,lung cancer patients,radiation pneumonitis (rp),radiotherapy,statistical methods,different risk group,classification algorithm,statistical analysis,learning (artificial intelligence),lung cancer patient,pattern classification,risk radiation-induced lung injury,lung,fatal side effect,feature selection strategy,machine learning techniques,dosimetry,cancer,classification,medical computing,feature selection,machine learning,classification algorithms,targeted therapy dosage,new method,predictive model,radio frequency,data mining,side effect,prediction model,niobium,learning artificial intelligence,support vector machines | Lung cancer,Feature selection,Targeted therapy,Computer science,Support vector machine,Radiation therapy,Artificial intelligence,Side effect,Statistical classification,Cancer,Machine learning | Conference |
ISBN | Citations | PageRank |
978-0-7695-3926-3 | 0 | 0.34 |
References | Authors | |
16 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jung Hun Oh | 1 | 61 | 13.11 |
| Rawan Al-Lozi | 2 | 0 | 0.68 |
| Issam El-Naqa | 3 | 528 | 36.31 |