1. Introduction

Big Data has been characterized by it three properties i.e.1.Volume, 2.Velocity and 3.Variety. Volume refers to the huge amount of data being generated by several sources. Velocity refers to the rate at which this data is being generated and the variety refers to the different type of the data being used [1]. Now a days with so much of data all around the world, the trend in healthcare is shifting from cure to prevention. Hospitals and healthcare systems are good repositories of big data (like patient records, test reports, medical images etc.) that can be

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and eproduction in any medium, provided the original work is properly cited. © 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

utilized to cut the cost in healthcare, to improve reliability and efficiency, and to provide better cure to patients.

Mohammad-Parsa Hosseini et al. [13] focused on an autonomic edge computing framework for processing of big data as part of a decision support system for surgical candidacy, an optimized model for estimation of the epileptogenic network, and an unsupervised feature

Adaptive Neural Network Classifier-Based Analysis of Big Data in Health Care

http://dx.doi.org/10.5772/intechopen.77225

127

Bernhard Schölkopf et al. [14] have designed a class of support vector algorithms for regres-

Chandra et al. [15] have proposed a approach for using MLP to handle Big data. There was high computational cost and time involved in using MLP for classification of Big data having large number of features. This is a promising technique for handling big data and is the idea

Huan Liu et al. [16] have introduced a concepts and algorithms of feature selection, surveys existing feature selection algorithms for classification and clustering, groups and compares different algorithms with a categorizing framework based on search strategies, evaluation

Malika Bendechache et al. [17] have proposed a distributed clustering approach to deal efficiently with both phases; generation of local results and generation of global models by

3. Proposed FCM Map-Reduce based adaptive neural network classifier

The large amounts of data, driven by record keeping, compliance & regulatory requirements, and patient care will historically render for the healthcare industry. While most data is saved in hard copy form, the current trend is towards quick digitization of these large amounts of data. Driven by mandatory requirements and the potential to develop the quality of healthcare delivery meanwhile minimizing the costs, these massive quantities of data known as 'big data' hold the promise of supporting a wide range of medical and healthcare functions, admitting between others clinical decision support, disease surveillance, and population health management. Some troubles that exist in big data analysis in health care are, i) to succeed, big data analytics in healthcare requires to be packaged so it is menu driven, user-friendly and transparent. ii) The lag among data collection and processing has to be addressed. iii) The crucial managerial issues of ownership, governance and standards have to be conceived. iv) Continu-

In the increasingly quick generation of large amounts of data, and across several areas of science, technological and conceptual advances are resulting. The collection and organization of data, the volume, variety, and velocity of current 'big data' production inaugurates novel opportunities and challenges in both scale and complexity these are always admitted on research. Also, in health care sector, the dealing of big data has currently get an interesting research topic, as since there are wide amount of medical data's available in cloud storage.

extraction model.

sion and classification.

extracted for the present research work.

for handling big data in health care

ous data acquisition and data cleansing is another issue.

criteria, and data mining tasks.

aggregation.

Healthcare applications require large amounts of computational and communication resources, and involve dynamic access to large amounts of data within and outside the health organization leading to the need for networked healthcare [2]. Data Analysis has always in demand in all the industry as it gives the approximate prediction of how the market is growing [3]. Although the innovations are in the healthcare field, there are some issues that need to be solved, particularly the heterogeneous data fusion and the open platform for data access and analysis [4].

Today, the healthcare industry is turning to big data technology to improve and manage medial systems. For this purpose, healthcare companies and organizations are leveraging big data in health informatics [5]. The analysis of big data carried out through different ways. Machine learning algorithm helps in analysis of big data very efficiently [3].

Feature selection is an important preprocessing technique used before data mining so that it can reduce the computational complexity of the learning algorithm and remove irrelevant/redundant features to remove noise [16]. Decision Tree is a predictive model of classification, which can be viewed as a Tree like structure [6]. It is simple and gives a fast and accurate result.

Neural Network is one of the other machine learning algorithms which showed a lot of modification. Neural Network is an adaptive learning model which adjusts the weight of the connecting links between its neuron [15]. K-Nearest Neighbor model of classification is one of the simplest classification algorithm which work on the classifying the data set based on the nearest neighbor of the existing class label of already trained mode [7]. Naïve Bayesian Classifier has a very good accuracy in classification for large set of data [8].

Clustering algorithm makes the groups or clusters of homogenous data. It is an unsupervised learning technique. In Partitioned Clustering the number of cluster was defined beforehand. In Hierarchical Clustering we do not need to define the number of clusters in advance [9, 10]. In both of the above approaches the stopping criterion is usually the number of clusters to be achieved; once the required number is achieved the algorithm can be stopped. Different methods are used for the analysis of Big Data in Health Care has been discussed below.

### 2. Literature survey

Abdulsalam Yassine et al. [11] have proposed a model that utilizes smart home big data as a means of learning and discovering human activity patterns for health care applications. They proposed the use of frequent pattern mining, cluster analysis and prediction to measure and analyze energy usage changes sparked by occupants' behavior.

Md. Mofijul Islam et al. [12] have proposed a mobility- and resource aware joint virtualmachine migration model for heterogeneous mobile cloud computing systems to improve the performance of mobile Smart health care applications in a Smart City environment.

Mohammad-Parsa Hosseini et al. [13] focused on an autonomic edge computing framework for processing of big data as part of a decision support system for surgical candidacy, an optimized model for estimation of the epileptogenic network, and an unsupervised feature extraction model.

utilized to cut the cost in healthcare, to improve reliability and efficiency, and to provide better

Healthcare applications require large amounts of computational and communication resources, and involve dynamic access to large amounts of data within and outside the health organization leading to the need for networked healthcare [2]. Data Analysis has always in demand in all the industry as it gives the approximate prediction of how the market is growing [3]. Although the innovations are in the healthcare field, there are some issues that need to be solved, particularly

Today, the healthcare industry is turning to big data technology to improve and manage medial systems. For this purpose, healthcare companies and organizations are leveraging big data in health informatics [5]. The analysis of big data carried out through different ways.

Feature selection is an important preprocessing technique used before data mining so that it can reduce the computational complexity of the learning algorithm and remove irrelevant/redundant features to remove noise [16]. Decision Tree is a predictive model of classification, which can be viewed as a Tree like structure [6]. It is simple and gives a fast and accurate result.

Neural Network is one of the other machine learning algorithms which showed a lot of modification. Neural Network is an adaptive learning model which adjusts the weight of the connecting links between its neuron [15]. K-Nearest Neighbor model of classification is one of the simplest classification algorithm which work on the classifying the data set based on the nearest neighbor of the existing class label of already trained mode [7]. Naïve Bayesian Classi-

Clustering algorithm makes the groups or clusters of homogenous data. It is an unsupervised learning technique. In Partitioned Clustering the number of cluster was defined beforehand. In Hierarchical Clustering we do not need to define the number of clusters in advance [9, 10]. In both of the above approaches the stopping criterion is usually the number of clusters to be achieved; once the required number is achieved the algorithm can be stopped. Different methods are used for the analysis of Big Data in Health Care has been discussed below.

Abdulsalam Yassine et al. [11] have proposed a model that utilizes smart home big data as a means of learning and discovering human activity patterns for health care applications. They proposed the use of frequent pattern mining, cluster analysis and prediction to measure and

Md. Mofijul Islam et al. [12] have proposed a mobility- and resource aware joint virtualmachine migration model for heterogeneous mobile cloud computing systems to improve the

performance of mobile Smart health care applications in a Smart City environment.

the heterogeneous data fusion and the open platform for data access and analysis [4].

Machine learning algorithm helps in analysis of big data very efficiently [3].

fier has a very good accuracy in classification for large set of data [8].

analyze energy usage changes sparked by occupants' behavior.

cure to patients.

126 Data Mining

2. Literature survey

Bernhard Schölkopf et al. [14] have designed a class of support vector algorithms for regression and classification.

Chandra et al. [15] have proposed a approach for using MLP to handle Big data. There was high computational cost and time involved in using MLP for classification of Big data having large number of features. This is a promising technique for handling big data and is the idea extracted for the present research work.

Huan Liu et al. [16] have introduced a concepts and algorithms of feature selection, surveys existing feature selection algorithms for classification and clustering, groups and compares different algorithms with a categorizing framework based on search strategies, evaluation criteria, and data mining tasks.

Malika Bendechache et al. [17] have proposed a distributed clustering approach to deal efficiently with both phases; generation of local results and generation of global models by aggregation.
