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Abstract

Data mining methods have the potential to identify groups at high risk. There are different steps of processing the data so as to extract their results consisting of data collection, data pre-processing, feature extraction, data partitioning, and data classification. There are different classification techniques like a classification tree, averaging tree, and machine learning algorithms. This paper explains the proposed model for cell survival/ death by using Random forest and boosting tree and random forest methods which are different Averaging tree techniques. The data is collected which is pre-processed by visual plots (basic statistics) and normality test (AD, KS and chi-square values). The marker proteins were selected from eleven different proteins by using statistical analysis (SER, p-value, and t-value). Lastly, averaging tree technique is applied to the data set to predict which protein or sample helps in cell survival/ death. In boosting tree, the division is on the basis of ten different concentrations of TNF, EGF, and Insulin while in RF method, the model is made for the training and testing of data on the basis of samples. 100-0-500 ng/ml yields the better results using boosting tree and from RF methods we come across that FKHR protein leads to cell death while rest proteins help in cell survival if they are present.

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Keywords

Averaging trees, boosted trees, random forests, marker proteins.