Name
Title | ||
|---|---|---|
Decision Fusion of Machine Learning Models to Predict Radiotherapy-Induced Lung Pneumonitis |
Abstract | ||
|---|---|---|
Combining different machine learning models (decision fusion) has been shown to be an effective method for estimating the underlying physical mechanism by allowing the models to reinforce each other when consensus exists, or, conversely, negate each other when there is no consensus. To be effective, decision fusion requires that the different models provide some degree of complementary information. In this work, we fuse the results of four different machine learning models (Boosted Decision Trees, Neural Networks, Support Vector Machines, Self Organizing Maps) to predict the risk of lung pneumonitis in patients undergoing thoracic radiotherapy. Fusion was achieved by simple averaging of the 10-fold cross validated predictions for each patient from all four models. To reduce prediction dependence on the manner in which the data set was split, 10-fold cross-validation was repeated 100 times for random data splitting. The area under the receiver operating characteristics curve for the fused cross-validated results was 0.79, higher than the individual models and with (generally) lower variance. The fusion extracted three important features as the consensus among all four models in predicting radiation pneumonitis risk: chemotherapy prior to radiotherapy, equivalent Uniform Dose (EUD) for exponent a = 1.2 to 3, and female gender. The results show great promise for machine learning in radiotherapy outcomes modeling. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ICMLA.2008.122 | ICMLA |
Keywords | Field | DocType |
lung pneumonitis,10-fold cross,predict radiotherapy-induced lung pneumonitis,different model,decision fusion,effective method,10-fold cross-validation,machine learning models,thoracic radiotherapy,different machine,radiotherapy outcome,correlation,decision trees,cross validation,radiotherapy,feature extraction,self organizing maps,machine learning,receiver operating characteristic curve,support vector machine,radiation therapy,boosted decision trees,neural nets,decision tree,modeling,neural network,predictive models,support vector machines,neural networks | Decision tree,Receiver operating characteristic,Pattern recognition,Computer science,Support vector machine,Self-organizing map,Feature extraction,Pneumonitis,Artificial intelligence,Artificial neural network,Alternating decision tree,Machine learning | Conference |
ISBN | Citations | PageRank |
978-0-7695-3495-4 | 0 | 0.34 |
References | Authors | |
9 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shiva K. Das | 1 | 5 | 1.04 |
| Shifeng Chen | 2 | 0 | 0.34 |
| Joseph O. Deasy | 3 | 105 | 14.98 |
| Sumin Zhou | 4 | 0 | 0.34 |
| Fang-Fang Yin | 5 | 14 | 5.24 |
| Lawrence B. Marks | 6 | 4 | 1.45 |