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ABSTRACT
➢ Due to machine learning 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 algorithm.
➢ It predicts probable diseases by mining data sets such as Covid-19, Chronic
Kidney disease and heart Disease. 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 machine learning disease risk prediction algorithm.
