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Robust multifocus deep neural network for progression prediction on patient trajectory data

International Journal of Intelligent Computing and Cybernetics 2022年第4期15卷 589-598页

作者： K.Arunkumar S.Vasundra Department of CSE JNTUAAnanthapuramIndia Department of CSE JNTUACEAAnanthapuramIndia

Purpose-Patient treatment trajectory data are used to predict the outcome of the treatment to particular disease that has been carried out in the *** order to determine the evolving disease on the patient and changes in the health due to treatment has not considered existing *** deep learning models to trajectory data mining can be employed to identify disease prediction with high accuracy and less computation ***/methodology/approach-Multifocus deep neural network classifiers has been utilized to detect the novel disease class and comorbidity class to the changes in the genome pattern of the patient trajectory data can be identified on the layers of the *** is employed to learn extracted feature set with activation and weight function and then merged on many aspects to classify the undetermined sequence of diseases as a new *** performance of disease progression learning progress utilizes the precision of the constituent classifiers,which usually has larger generalization benefits than those optimized ***-deep learning architecture uses weight function,bias function on input layers and max *** of the input layer has applied to hidden layer to generate the multifocus characteristics of the disease,and multifocus characterized disease is processed in activation function using ReLu function along hyper parameter tuning which produces the effective outcome in the output layer of a fully connected *** results have proved using cross validation that proposed model outperforms methodologies in terms of computation time and ***/value-Proposed evolving classifier represented as a robust architecture on using objective function to map the data sequence into a class distribution of the evolving disease class to the patient ***,the generative output layer of the proposed model produces the progression outcome of the disease of the particular pa

关键词： Patient trajectory data deep learning representatives Disease evolution Unstructured hierarchical data Cluster reconstruction error

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