In for monitoring patient’s details. Qiu et al proposed

In
our ancestor’s life, the risk of chronic diseases is very less because of their
life style. On that day, the physical activities are more in their work for both
men and women. And also their food culture is very natural. But now days due to
changes in life styles, working environment, food habits and less physical
activity the people are affected by many problems. The changes in life style,
the diseases are also increasing. Many countries have a problem in controlling
diseases in particular time period. More percentage of death is caused by
diseases.

There
are number of ways to predict diseases. Chen et al proposed a new system using
smart clothing for monitoring patient’s details. Qiu et al proposed electronic
health record (EHR) to maintain patient’s details. Wang et al proposed an
algorithm for PHR (Personal health record) based distributed system. Bates et
al proposed six applications of big data for healthcare.

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Prediction
involves a machine learning algorithm. It can use supervised learning algorithm
for training data with labels to train the model. The test set can be
classified into two groups that may be high risk or low risk. If the data set
is small means the characteristics are selected through experience.

In
previous research work, the researchers’ concentrates only structured data.
Development in big data, we cannot use only selected characteristics. Because of
climate and living habits in particular region, the diseases may be differing.

There
are some challenges in big data analysis.

1.     
How should the missing data be
addressed?

2.     
How should the main chronic diseases in
certain region?

3.     
How to determine the main characteristics
of diseases in the region be determined?

4.     
How can big data analysis technology be
used to analyze the diseases?

5.     
How to create better model?

We
can use latent factor model to reconstruct the missing data. Then use
statistical knowledge, to determine the major electronic diseases in the
region.