Mining Health Data in Multimodal Data Series for Disease Prediction

R.Anupriya ¹ , P.Saranya ² , R.Deepika ³

1. Computer science and engineering, Sree Sakthi Engineering College,Anna University, Coimbatore, India.
2. Computer science and engineering, Sree Sakthi Engineering College,Anna University, Coimbatore, India.
3. Computer science and engineering, Sree Sakthi Engineering College,Anna University, Coimbatore, India.

Section:Research Paper, Product Type: Isroset-Journal
Vol.6 , Issue.2 , pp.96-99, Apr-2018

CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v6i2.9699

Online published on Apr 30, 2018

Copyright © R.Anupriya, P.Saranya, R.Deepika . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Disease Prediction plays a major role in health care community. With data mining process, disease will be predicted from large number of data. Dataset may be structured or unstructured. If the dataset is unstructured then the latent factor model is used to convert unstructured to structured data and it is very complex to predict a disease using unstructured data. Therefore we use synthetic data, which is structured. We concentrate on different kind of diseases. We propose a convolutional neural network based multimodal disease risk prediction (CNN-MDRP). Here datasets are stored as HER records. K-means clustering algorithm is used to group the datasets. Semi-Supervised Heterogeneous algorithm is applied to grouped data to predict the disease.

Key-Words / Index Term :
Data Mining, Disease Prediction, HealthCare, Multimodal, K-means, SVM classification

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