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@article{154168,
        author = {Lithu Mathew and Kala O S},
        title = {Machine Learning in Smart HealthCare Industry},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {7},
        pages = {27-31},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=154168},
        abstract = {Chronic disease prediction plays an important role in healthcare informatics. It is crucial to diagnose the chronic disease at an early stage. In the field of healthcare communities, the accurate analysis and prediction plays the major role tofind out the risk of the disease in the patient. However, the analysis of accuracy is reduced, and it leads to less accuracy of prediction when the quality of data is incomplete and the poor condition of the medical data. We seek machine learning techniques for effective prediction of chronic disease. We propose to use convolution neural network algorithm for structured and unstructured data.  The  accuracy  obtained using CNN  model reaches 94.8% and none of the existing algorithm focuses on structured and unstructured data in the area ofmedical data analytics.},
        keywords = {Convolution neural network, structured data, Data imputation},
        month = {},
        }