Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: This study aims to employ deep learning for the development of an automatic classifier for the severity of chronic obstructive pulmonary disease (COPD) in patients. A three-layer deep belief network (DBN) with two hidden layers and one visible layer was employed to generate a model for classification, and the model's robustness against exacerbation was analyzed. Subjects from the COPDGene cohort re staged using the GOLD 2011 guidelines. 10,300 subjects with 361 features each re included in the analysis. After feature selection and parameter optimization, the proposed classification method achieved an accuracy of 97.2% by using a 10-fold cross validation experiment. The most sensitive features as revealed by the DBN ights re consistent with the clinical consensus as per previous studies and clinical diagnosis rules. In summary,  demonstrate that the DBN is a competitive tool for exacerbation risk assessment for patients suffering from, COPD.
['Jun Ying', 'Joyita Dutta', 'Ning Guo', 'Lei Xia', 'Arkadiusz Sitek', 'Quanzheng Li']
I am writing a research paper on a idea Gold classification of COPDGene cohort based on deep learning
[{"from": "human", "value": "I am writing a research paper on a idea Gold classification of COPDGene cohort based on deep learning"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: This study aims to employ deep learning for the development of an automatic classifier for the severity of chronic obstructive pulmonary disease (COPD) in patients. A three-layer deep belief network (DBN) with two hidden layers and one visible layer was employed to generate a model for classification, and the model's robustness against exacerbation was analyzed. Subjects from the COPDGene cohort re staged using the GOLD 2011 guidelines. 10,300 subjects with 361 features each re included in the analysis. After feature selection and parameter optimization, the proposed classification method achieved an accuracy of 97.2% by using a 10-fold cross validation experiment. The most sensitive features as revealed by the DBN ights re consistent with the clinical consensus as per previous studies and clinical diagnosis rules. In summary,  demonstrate that the DBN is a competitive tool for exacerbation risk assessment for patients suffering from, COPD."}]