Abstract
The accurate prediction of chronic diseases at an early stage can help the patients to plan the future course of treatments effectively so as to avoid any life threatening situations. Machine learning techniques provide cost and computationally inexpensive solutions for multi-label as well as multi class disease classification problems. In case of multi-label classification problems more intelligent techniques of data analytics are required due to the presence of correlations and label dependencies in the dataset. The accuracy of prediction also depends upon the representation of the data. In this paper a Sparse Autoencoder and Deep learning based Framework has been proposed to predict the presence of multiple diseases in the patients. The framework consists of three phases. In the first phase, Sparse Autoencoder is used to learn features from the original dataset. In the second phase, Label Powerset method is used to transform the problem from multi-label to multiclass classification problem. In third and the final stage, a deep learning architecture is applied. 80.85% of accuracy has been achieved by the proposed framework.
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Acknowledgements
The authors of this paper thank the Collaborative Innovation Center on Internet Healthcare and Health Service of Henan Province, Zhengzhou University, for providing the dataset, available online at http://pinfish.cs.usm.edu/dnn/.
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Ramotra, A.K., Mahajan, A., Mansotra, V. (2022). Sparse Autoencoder and Deep Learning Based Framework for Multi-label Classification of Chronic Diseases. In: Rathore, V.S., Sharma, S.C., Tavares, J.M.R., Moreira, C., Surendiran, B. (eds) Rising Threats in Expert Applications and Solutions. Lecture Notes in Networks and Systems, vol 434. Springer, Singapore. https://doi.org/10.1007/978-981-19-1122-4_11
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DOI: https://doi.org/10.1007/978-981-19-1122-4_11
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