Interactive Disease Diagnosis and Decision Support Mechanism with Concept Hierarchy

Paper ID: EIJTEM_2016_3_1_74-77

Author's Name: V. Nandhini, R. Pathmavathi, R. Janani and J.V. Bhavithra

Volume: 3

Issue: 1

Year: 2016

Page No: 74-77

Abstract:

Web based disease diagnosis scheme is provided to make decisions with the support of interactive diagnosis model. Community based health services supports automatic disease inference identification for online health seekers. Question and Answer (QA) sessions are suffered with the vocabulary gap and incomplete information. Correlated medical concepts and limited high quality training samples makes an impact on inferring results. Diseases and symptoms are collected and used in the QA based health analysis tasks. Deep learning scheme is applied to infer the possible diseases using QA data values. Global leaning component is used to mine the discriminant medical signatures from raw features. In local learning raw features and their signatures are updated into the input layer and hidden layer. Sparsely connected deep learning scheme is applied to infer various kinds of diseases. The sparse deep learning scheme is enhanced to fetch discriminant features from health data values. Medical terminology based Ontology is used for inference estimation process. Feature analysis is carried out with the conceptual relationship based weight values. Question and Answer (QA) data values are evaluated with symptom priority levels

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