4.2.5. Accuracy

The method with high accuracy is said to be more effective. With other being k-means based Map-Reduce and DBSCAN method, for the proposed FCM based Map-Reduce model raises the accuracy while comparing.

Figure 7 establishes the accuracy for the proposed FCM based Map-Reduce model and k-means based Map-Reduce and DBSCAN model while the number of records rises. This clearly demonstrates that the proposed FCM based Map-Reduce model raises the accuracy while equating with other accomplishing k-means based Map-Reduce and DBSCAN method.

### 4.3. Case (2): performance analysis by varying number of Mapper

### 4.3.1. Time

Figure 5 establishes the precision level for our proposed FCM based Map-Reduce model and kmeans based Map-Reduce and DBSCAN model while the number of records rises. This clearly demonstrates that our proposed FCM based Map-Reduce model rises the precision level while

The method with high recall is said to be more effective. The proposed FCM based Map-Reduce model rises the recall while equating with other being k-means based Map-Reduce and DBSCAN

While the number of records rises, the Figure 6 establishes the recall for the proposed FCM based Map-Reduce model and k-means based Map-Reduce and DBSCAN model. This clearly

equating with other being k-means based Map-Reduce and DBSCAN method.

Figure 6. Recall for FCM based Map-Reduce; k-means based Map-Reduce and DBSCAN model.

Figure 5. Precision for FCM based Map-Reduce; k-means based Map-Reduce and DBSCAN model.

4.2.4. Recall

138 Data Mining

method.

In the medical data records, an effective method should decrease the time needed to predict the presence of abnormality. The proposed FCM based Map-Reduce model decreases the time while equating with other accomplishing k-means based Map-Reduce and DBSCAN method.

Figure 8 establishes the time needed for prediction of abnormality applying the proposed FCM based Map-Reduce model and k-means based Map-Reduce model while the number of mapper rises. This clearly demonstrates that the proposed FCM based Map-Reduce model decreases the time needed for prediction of abnormality while equating with other accomplishing k-means based Map-Reduce method.

Figure 7. Accuracy for FCM based Map-Reduce; k-means based Map-Reduce and DBSCAN model.

Figure 8. Time taken by FCM based Map-Reduce; k-means based Map-Reduce model for prediction.

### 4.3.2. Memory

An effective method should decrease the requirement of memory. While equating with other accomplishing k-means based Map-Reduce and DBSCAN method, for the proposed FCM based Map-Reduce model decreases the requirement of memory.

4.3.3. Precision

Reduce method.

4.3.4. Recall

Map-Reduce method.

4.3.5. Accuracy

the accuracy while comparing.

The method with high precision will be more effective. The proposed FCM based Map-Reduce model raises the precision while equating with other accomplishing k-means based Map-

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Figure 10. Precision for FCM based Map-Reduce; k-means based Map-Reduce and DBSCAN model.

While the number of mapper rises, Figure 10 establishes the precision level for the proposed FCM based Map-Reduce model and k-means based Map-Reduce model. While equating with other accomplishing k-means based, Map-Reduce method this clearly demonstrates that the

The method with high recall is said to be more effective. Our proposed FCM based Map-Reduce model raises the recall while comparing with other accomplishing k-means based

Figure 11 establishes the recall for the proposed FCM based Map-Reduce model and k-means based Map-Reduce and DBSCAN model while the number of mapper rises. This clearly demonstrates that the proposed FCM based Map-Reduce model raises the recall while equat-

The method with high accuracy is said to be more effective. With other being k-means based Map-Reduce and DBSCAN method, for the proposed FCM based Map-Reduce model raises

While the number of mapper increases, the Figure 12 establishes the accuracy for the proposed FCM based Map-Reduce model and k-means based Map-Reduce and DBSCAN model. This

ing with other accomplishing k-means based Map-Reduce and DBSCAN method.

proposed FCM based Map-Reduce model rises the precision level.

While the number of mapper rises, Figure 9 establishes the memory needed for the proposed FCM based Map-Reduce model and k-means based Map-Reduce model and k-means base model. This clearly demonstrates that the proposed FCM based Map-Reduce model decreases the memory requirement while equating with other accomplishing k-means based Map-Reduce and DBSCAN method.

Figure 9. Memory requirement for FCM based Map-Reduce; k-means based Map-Reduce and DBSCAN model.

Figure 10. Precision for FCM based Map-Reduce; k-means based Map-Reduce and DBSCAN model.

### 4.3.3. Precision

4.3.2. Memory

140 Data Mining

Reduce and DBSCAN method.

An effective method should decrease the requirement of memory. While equating with other accomplishing k-means based Map-Reduce and DBSCAN method, for the proposed FCM

Figure 8. Time taken by FCM based Map-Reduce; k-means based Map-Reduce model for prediction.

While the number of mapper rises, Figure 9 establishes the memory needed for the proposed FCM based Map-Reduce model and k-means based Map-Reduce model and k-means base model. This clearly demonstrates that the proposed FCM based Map-Reduce model decreases the memory requirement while equating with other accomplishing k-means based Map-

Figure 9. Memory requirement for FCM based Map-Reduce; k-means based Map-Reduce and DBSCAN model.

based Map-Reduce model decreases the requirement of memory.

The method with high precision will be more effective. The proposed FCM based Map-Reduce model raises the precision while equating with other accomplishing k-means based Map-Reduce method.

While the number of mapper rises, Figure 10 establishes the precision level for the proposed FCM based Map-Reduce model and k-means based Map-Reduce model. While equating with other accomplishing k-means based, Map-Reduce method this clearly demonstrates that the proposed FCM based Map-Reduce model rises the precision level.

### 4.3.4. Recall

The method with high recall is said to be more effective. Our proposed FCM based Map-Reduce model raises the recall while comparing with other accomplishing k-means based Map-Reduce method.

Figure 11 establishes the recall for the proposed FCM based Map-Reduce model and k-means based Map-Reduce and DBSCAN model while the number of mapper rises. This clearly demonstrates that the proposed FCM based Map-Reduce model raises the recall while equating with other accomplishing k-means based Map-Reduce and DBSCAN method.

### 4.3.5. Accuracy

The method with high accuracy is said to be more effective. With other being k-means based Map-Reduce and DBSCAN method, for the proposed FCM based Map-Reduce model raises the accuracy while comparing.

While the number of mapper increases, the Figure 12 establishes the accuracy for the proposed FCM based Map-Reduce model and k-means based Map-Reduce and DBSCAN model. This

based Mapreduce model is equated with the accomplishing k-means based Mapreduce and DBSCAN model and tested in terms of different evaluates like time, memory, precision, recall and accuracy by differentiating the data size as well as the number of mappers. It can be seen from the results that, all the values found for the proposed method is better when equated to the being method. Moreover, the time and memory requirements are very much minimized when the number of mappers is raised. This establishes the efficiency of proposed model and so the proposed application can be applicable for handling large healthcare databases in

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\*Address all correspondence to: mrs.manaswini.pradhan@gmail.com

2 P.G. Department of ICT, Fakir Mohan University, India

International Conference on. IEEE, 2013

1 Visiting Researcher School of Computer Science and Software Engineering, East China

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[5] Fang R, Pouyanfar S, Yang Y, Chen S-C, Iyengar SS. Computational health informatics in the big data age: A survey. ACM Computing Surveys (CSUR). July 2016;49(1):1-36

[6] Wang Dingxian, Xiao Liu, and Mengdi Wang. "A DT-SVM strategy for stock futures prediction with big data." Computational Science and Engineering (CSE), 2013 IEEE 16th

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Author details

References

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Manaswini Pradhan1,2\*

Normal University, Shanghai, China

Figure 11. Recall for FCM based Map-Reduce; k-means based Map-Reduce and DBSCAN model.

Figure 12. Accuracy for FCM based Map-Reduce; k-means based Map-Reduce and DBSCAN model.

clearly demonstrates that the proposed FCM based Map-Reduce model raises the accuracy while equating with other accomplishing k-means based Map-Reduce and DBSCAN method.

### 5. Conclusion

The presented research method have improved a FCM based Mapreduce programming model for the implementation parallel calculating applying Adaptive Artificial Neural Network approach for the prediction of abnormality of medical records. The proposed FCM based Mapreduce model is equated with the accomplishing k-means based Mapreduce and DBSCAN model and tested in terms of different evaluates like time, memory, precision, recall and accuracy by differentiating the data size as well as the number of mappers. It can be seen from the results that, all the values found for the proposed method is better when equated to the being method. Moreover, the time and memory requirements are very much minimized when the number of mappers is raised. This establishes the efficiency of proposed model and so the proposed application can be applicable for handling large healthcare databases in cloud environment.
