Big Data Analytics and Machine Intelligence in Biomedical and Health Informatics

In the survey paper [22] Archenaa et al . clearly mentioned that two critical issues in the healthcare and medical analytics sphere in regard to big data are the privacy of patients and the protection of personal data and information. While each country views this information as having a rightful claim to medical data, medical data are indeed very private and confidential, and no information is really safe [23]. A solution to these difficulties, which means the software should be used advanced and able to obfuscate identity data, needs to use effective encryption algorithms and, alternatively, anonymize itation. In order to achieve all of these goals, software should offer enhanced security and strict access controls that give the end users/clients the appropriate levels of protection, it must also have excellent governance controls that make sure user privacy and control is maintained.

The use of big data analytics in medicine and healthcare is incredibly powerful, productive, interesting, and diverse. It integrates heterogeneous data like medical records, experimental, electronic health, and social data in order to explore the relations among the different characteristics and traces of data points like diagnoses and medication dosages, along with information such as public chatter to derive conclusions about outcomes. More diverse data needs to be combined into big data analysis, such as biosciences, sensor informatics, medical informatics, bioinformatics, and health computational biomedicine to get the truth out of its information.

As a future endeavor, the characteristics of big data provide an excellent foundation for developing applications that can handle big data in medicine and healthcare using promising software platforms. One such platform is Apache Hadoop Map Reduce, an open-source distributed data processing platform that makes use of massive parallel processing (MPP). These applications should enable data mining techniques to be applied to these heterogeneous and complex data in order to uncover hidden patterns and novel knowledge. Recent advancements in processor technology, newer types of memories, and network architecture will reduce the time required to transfer data from storage to the processor in a distributed environment.

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1 *Email: anil.pise@thefinalmile.com

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