Abstract: A computing device of the present invention estimates an unknown parameter ? of a model formula when distributed sample data is acquired, wherein when an estimated quantity of ? is acquired, a function g is estimated using a random forest model, such that when an estimated quantity of g is acquired, the estimated quantity of g and the estimated quantity of ? are used so as estimate a function G as a prediction formula for new data corresponding to a specific item such that an estimated quantity of G is acquired, and thus new data xnew is received, thereby enabling the class of the specific item to be classified from the calculated value.

Abstract: A method for diagnosing lung cancer in a subject by using a complex biomarker group is provided. The method includes steps of: (a) a computing system (1) acquiring a model M by using expression level data by individual biomarkers in the complex biomarker group and then (2) acquiring expression level data by the individual biomarkers measured from a biological specimen of the subject or their processed data Bk; and (b) the computing system determining whether lung cancer is detected in the subject by using the acquired data of the subject by referring to the model M; wherein the complex biomarker group includes CEA, HE4, ApoA2, TTR, sVCAM-1 and RANTES.

Abstract: A computing device of the present invention estimates an unknown parameter ? of a model formula when distributed sample data is acquired, wherein when an estimated quantity of ? is acquired, a function g is estimated using a random forest model, such that when an estimated quantity of g is acquired, the estimated quantity of g and the estimated quantity of ? are used so as estimate a function G as a prediction formula for new data corresponding to a specific item such that an estimated quantity of G is acquired, and thus new data xnew is received, thereby enabling the class of the specific item to be classified from the calculated value.

Abstract: A method for diagnosing lung cancer in a subject by using a complex biomarker group is provided. The method includes steps of: (a) a computing system (1) acquiring a model M by using expression level data by individual biomarkers in the complex biomarker group and then (2) acquiring expression level data by the individual biomarkers measured from a biological specimen of the subject or their processed data Bk; and (b) the computing system determining whether lung cancer is detected in the subject by using the acquired data of the subject by referring to the model M; wherein the complex biomarker group includes CEA, HE4, ApoA2, TTR, sVCAM-1 and RANTES.