to download project abstract

Hospital recommendation System is considered as an important factor in health care
sector for managing the administrative, financial and clinical aspects of a hospital. Due to data
mining progress in biomedical and healthcare communities, accurate study of medical data
benefits early disease recognition, patient care and community services. When the quality of
medical data is incomplete the exactness of study is reduced. Moreover, different regions exhibit
unique appearances of certain regional diseases, which may results in weakening the prediction of
disease outbreaks. In the proposed system, it provides machine learning algorithms for effective
prediction of various disease occurrences in disease-frequent societies. It experiment the altered
estimate models over real-life hospital data collected. To overcome the difficulty of incomplete
data, it use a latent factor model to rebuild the missing data. It experiment on a regional chronic
illness of cerebral infarction. Using structured and unstructured data from hospital it use Machine
Learning Decision Tree algorithm. It predicts probable diseases and hospitals by mining data sets.
To the best of our knowledge in the area of medical big data analytics none of the existing work
focused on both data types. Compared to several typical estimate algorithms, the calculation
exactness of our proposed algorithm reaches 94.8% with a convergence speed which is faster
than that of the Decision tree disease risk prediction algorithm.

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