Machine Learning in Healthcare Data Analysis
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.
A different type of data is present in healthcare. To analyze this variety of data various Machine learning algorithms such as supervised, unsupervised and reinforced algorithms are used to improve prediction which can be analyzed using various performance parameters like accuracy, sensitivity, specificity, precision, F1 score, and Area under Curve. In this paper, machine learning algorithms are defined and use of machine learning algorithms for analyzing different types of healthcare data like clinical, omics and sensor data is done. From the survey, it is concluded that for analyzing different types of data in healthcare, various machine learning algorithms and feature extraction techniques are proposed by various authors for survival prediction of cancer patients.
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