This document presents a framework for deploying a cloud-based clinical decision support system (CDSS) using secured health data mining. The framework uses Apache Hadoop components like HDFS and MapReduce to efficiently process and analyze big healthcare data for predicting heart diseases. Hadoop clusters are deployed on Google Cloud Storage for scalable data processing. Classification and clustering methods are used to generate decision trees from reduced attribute datasets for building the knowledge-based CDSS. The proposed system aims to enhance data security by encrypting patient records stored in Google Cloud Storage buckets.

1. A Seminar On
Cloud Deployable Health Data Mining Using
Secured Framework For Clinical Decision Support
System
Guided By: Presented By:
Prof. B. R. Bombade Hanwate Avinash M.
2016MNS013

2. CONTENTS
 INTRODUCTION
 PREVIOUS RESEARCH
 METHODS
 RESULTS
 PROPOSED SYSTEM
 CONCLUSION
 REFERENCE

3. INTRODUCTION
 Reliable , scalable, and secured framework is designed.
 Components of Apache Hadoop are used for processing of big data
used for prediction.
 Hadoop clusters are deployed on Google cloud storage.
 Mapreduce based classification via clustering method is proposed for
efficient classification of instances using reduced attributes.

4. What is CDSS?
 CDSS are developed for the early detection of Heart diseases.
 Medical errors are reduced using CPG’s.
 The five rights considered for implementing successful CDSS: Right
Information, Right People, Right Channel, Right Intervention
Format, Right Time.
 There are two types of CDSS, Knowledge based CDSS and Non-
knowledge based CDSS.

5.  Interoperability is capability of different systems to exchange
information regarding patients with each other for providing
effective treatment.
Fig. Working of CDSS

6. Cloud Based CDSS
 Cloud computing platform provide High performance computing.
 Flexible , scalable, and efficient data storage services are offered by
cloud.
 Confidential records of patients stored in clouds must comply with
HITECH standards.

7. Previous Research
 Used as motivation in present research.
 Knowledge based CDSS was designed using c 4.5 decision tree algorithm
and diseases were predicted with 61.0734% accuracy.
 For analysis of big data over Apache Hadoop platform classification and
clustering methods were used.
 CDSS managed on private cloud was used for storing records of patients
securely.
 The security of records stored on SQL server was ensured using digital
signature.

9. Methods
 Classification and Classification via clustering approach are used for
prediction of Heart diseases.
 14 attributes are reduced to 3 (cp, ca, thal).
 Steps followed for analysis of datasets are demonstrated as:
Fig. Stages for Analysis of data

10. Framework used
 Datasets are analyzed on WEKA and Hadoop platform.
 .Arff files are loaded into WEKA explorer for WEKA platform.
 HDFS and Mapreduce are used for Hadoop platform.
 HDFS explorer is used instead of command line prompt for storing
files.
 Map and Reduce functions are generated using Mappers and
Reducers for performing computation over big data effectively.

11. Proposed Algorithm
 Using this Mapreduce based algorithm decision tree is generated
based on the calculations of entropy and gain ratio of attributes.
 All instances of input files are splitted into index and value.
 output files are generated for every node as intermediate files.
 Intermediate files are merged using combiner and single output rule
file for decision tree is generated.

12.  Flowchart:

13. Results
 Classification using WEKA:
 In classification approach decision trees are generated by
J48 classifier using training set method.
 In classification via clustering approach instances clustered
using k-means are used as input by J48 classifier.

14.  Classification using Mapreduce:
 Classification of reduced attributes (cp, ca, thal) is done using
Mapreduce based c 4.5 decision tree algorithm.
 The results of classification are compared on the basis of
execution time required for generating decision tree.
 Based on demonstration of WEKA and mapreduce based decision
trees, it is found that mapreduce based decision trees are better.

15. Hadoop clusters on cloud
 Hadoop clusters are deployed on GCS using GCE with help of GCS
connector.
 Mapreduce jobs are run using GCE with created virtual machine
instances.
 For faster processing input and output files are stored on bucket of
GCS.
Fig. Mapreduce processing using GCE

16. Proposed System
 In proposed system cloud based framework, security of data stored in
buckets of GCS is enhanced by restricting access to stored data.
 Records of patients are stored in encrypted form in .SEQ files.
 These files can further be encrypted with 256-bit AES scheme using
NppCrypt plugin of Notepad++.

17. Conclusion
 Framework for mining big data to predict Heart diseases accurately
with reduced attributes is designed.
 Inference rules for building knowledge based CDSS are generated by
traversing nodes of accurate mapreduce based decision trees.
 These trees are generated by classification via clustering approach
and are more accurate as compared to WEKA’s decision trees.
 Combiner is added between Mapper and Reducer to improve the
performance of c 4.5 decision tree algorithm.

18. References
[1] Ahmed, A. & Hannan, S. A. (Sept. 2012). “Data Mining Techniques
to Find Out Heart Diseases: An Overview”. IJITEE, Vol. 1(4), pp.
18-23.
[2] AL-Gamdi, A. A. et al. (May. 2014). "Clinical Decision Support
System in HealthCare Industry Success and Risk Factors”. IJCTT,
Vol.11(4), pp. 188-192.
[3] Ayma, V. A. et al. (Mar. 2015). “Classification Algorithms for Big
Data Analysis, a Map Reduce Approach”. The International Archives
of the Photogrammetry, Remote Sensing and Spatial Information
Sciences (ISPRS), Vol. XL-3/W2, pp. 17-25.
[4] Kamalraj, N. & Malathi, A. (Nov. 2014). ”Hadoop Operations
Management for Big Data Clusters in Telecommunication Industry”.
International Journal of Computer Applications. Vol. 105(12), pp. 40-
44.