Main Reference PaperRecurrent Convolutional Neural Network based Multimodal Disease Risk Prediction, Future Generation Computer Systems, 2019 [Python]
  • The proposed Recurrent Convolutional Neural Network (RCNN)-based Multimodal Disease Risk Prediction (RCNN-MDRP) algorithm exploits both the structured and the unstructured data for the effective disease risk prediction. By employing the RCNN, the proposed algorithm efficiently retrieves the fine-grained features of the textual unstructured data of patients. It exploits the deep belief network to obtain the non-linear relation between the data.

Description
  • The proposed Recurrent Convolutional Neural Network (RCNN)-based Multimodal Disease Risk Prediction (RCNN-MDRP) algorithm exploits both the structured and the unstructured data for the effective disease risk prediction. By employing the RCNN, the proposed algorithm efficiently retrieves the fine-grained features of the textual unstructured data of patients. It exploits the deep belief network to obtain the non-linear relation between the data.

  • To predict the risk of the disease based on the multimodal data

  • To capture the non-linear relation between the data using the deep learning technique

Aim & Objectives
  • To predict the risk of the disease based on the multimodal data

  • To capture the non-linear relation between the data using the deep learning technique

  • To develop the system with the optimal feature reduction technique provides the faster operation and the lesser memory consumption

Contribution
  • To develop the system with the optimal feature reduction technique provides the faster operation and the lesser memory consumption

  • M.E / M.Tech/ MS / Ph.D.- Customized according to the client requirements.

Project Recommended For
  • M.E / M.Tech/ MS / Ph.D.- Customized according to the client requirements.

  • No Readymade Projects-project delivery Depending on the complexity of the project and requirements.

Order To Delivery
  • No Readymade Projects-project delivery Depending on the complexity of the project and requirements.

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