Journal of Biology and Today's World

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Machine Learning in Healthcare Data Analysis: A Survey

Abstract

Author(s): Arwinder Dhillon* and Ashima Singh

In recent years, healthcare data analysis is becoming one of the most promising research areas. Healthcare includes data in various types such as clinical data, Omics data, and Sensor data. Clinical data includes electronic health records which store patient records collected during ongoing treatment. Omics data is one of the high dimensional data comprising genome, transcriptome and proteome data types. Sensor data is collected from various wearable and wireless sensor devices. To handle this raw data manually is very difficult. For analysis of data, machine learning is emerged as a significant tool. Machine learning uses various statistical techniques and advanced algorithms to predict the results of healthcare data more precisely. In machine learning different types of algorithms like supervised, unsupervised and reinforcement are used for analysis. In this paper, different types of machine learning algorithms are described. Then use of machine learning algorithms for analyzing various healthcare data are surveyed.

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