Feature Selection is one of the preprocessing steps in machine learning tasks. Feature Selection is effective in reducing the dimensionality, removing irrelevant and redundant feature. In this paper, we propose a new feature selection algorithm (Sigmis) based on Correlation method for handling the continuous features and the missing data. Empirical comparison with three existing feature selection algorithms using UCI data sets shows that the proposed system is very effective and efficient in selecting the feature set.
One of the most appealing IoT application areas is
medical care and health care. This promising technology is
reshaping current health-care service that comply with treatment
and mediation at home. The core part of IoT constitutes sensors
and various devices for diagnosis and imaging. Now-a-days
sensors are becoming smaller, allowing them to be worn without
interfering with daily activities.. To make sensors wearable and
wireless, it should be small in dimensions and also the energy,
memory, and processing power available also matters. Health
services dependent on the Internet of Things are supposed to
minimise cost, enhance the user's experience and improve their
quality of life. IoT has many hurdles in its implementation,
security is the most important. This paper throws light on the
different methods of securing the medical sensitive data through
the network.
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