Priyanka Bhalerao, Sayali Ekbote, Kalyani Jainak, Nikita Vaidya


In today’s modern world, healthcare also needs to be modernized. It means that the healthcare data should be properly analyzed so that we can categorize it into groups of Gender, Disease, City, Symptoms and treatment. The gigantic size of analytics will need large computation which can be done with the help of distributed processing HADOOP. The frameworks use will provide multipurpose beneficial outputs which includes getting the healthcare data analysis into various forms. BIGDATA is used to predict epidemics, cure disease, improve quality of life and avoid preventable deaths. With the increasing population of the world, and everyone living longer, models of treatment delivery are rapidly changing and many of the decision behind those changes are being driven by data. The drive now is to understand as much as a patient as possible, as early in their life as possible, hopefully picking up warning signs of serious illness at early enough stage that treatment is far simpler and less expensive than if it had not been spotted until later. The proposed system will group together the disease and their symptoms data and analyze it to provide cumulative information. After the analysis, algorithm could be applied to the resultant and grouping can be made to show a clear picture of the analysis. The groups made by the system would be symptoms wise, age wise, gender wise, season wise, disease wise etc. As the system will display the data group wise, it would be helpful to get a clear idea about the disease and their rate of spreading, so that appropriate treatment could be given at proper time.


Big Data, NoSql, Temporal Event Analysis, Medical Record, MapReduce

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