Healthcare Big Data Analysis Paper

As the healthcare is increased day by day, it is very difficult to analysis the big and huge amount of the datasets. The healthcare data consists of the medicines data like drug molecules and structures and clinical trials, environment factors related to the health, lab reports, health insurance, and global disease survey etc. The healthcare big data analysis is the three step process:

1. Preprocessing

2. Cleaning

3. Visualization According to paper [12] healthcare big data is analyzed by using the open source platform-Hadoop. Here we consider the three diseases. The Hadoop is the apache top level, open-source implementation of frameworks for reliable, scalable, distributed computing and data storage. Basically, Hadoop is the platform

Then the other table is created in the MS-Excel. The newly created table is processed in the HIVE by using HQL data manipulation command. Then the tabular data is graphically plotted by using ‘R’ tool and identify the patterns based the age and gender of the patient. [12] The healthcare data in the particular disease like HIV/AIDS is analyze and archive through the tool MonogoDB and by using data mining technique that is k-Means algorthirm.With the help of the world health organization we identify the dataset of the occurrence of HIV/AIDS .Then form the groups of estimated numbers of people living in the different countries suffering from the HIV/AIDS. The Clusters of the dataset are formed with the help of the data mining technique that is K-Means algorithm which is explained as

5) Recalculate the distance between each data point and new obtained cluster centers.

6) If no data point was reassigned then stop, otherwise repeat from step 3.

By using the k-means algorthrim determines the initial and final cluster centers of the estimated number of people suffering from the HIV/AIDS based on the year. After the clustering of the dataset import the data into big data tool that is in the MonogoDB for manage, archive, analyze and store the data and find the final result .[8] Instead of use of the clustering technique, we can also use the genetic technique of the data mining .According to the paper[9],big data application is used to analyze and archive the mental health data ,with the help of the data mining algorithm that is genetic algorithm and the big data tool that is MonogoDB. The data of the mental health is collected from the world health organization and then mined through data mining algorithm-genetic algorithm and imported into the MonogoDB. The genetic algorithm is used for finding the optimal solution.

Genetic