Medical Data Mining Using Different Classification and Clustering Techniques: A Critical Survey

Sharma, Richa; Singh, Sanjiv; Khatri, Sujata

doi:10.1109/cict.2016.142

Cited by 21 publications

(12 citation statements)

References 11 publications

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“…It can be overcome by data de-duplication process, it identifies the unique chunks of data and stored in a memory and it referred the remaining chunks in the dataset when the redundant chunks occur it replaced with a small reference from the stored chunks. Data repartition partition the data based on some characteristics of data such as content aware, computation aware or network aware [4] [5].…”

Section: A Backgroundmentioning

confidence: 99%

Improving Mapreduce Process By Introducing Aggregator Repartition Data for Big Data Analytics

Kumar¹,

Ramkumar²,

Shankar³

et al. 2019

IJEAT

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This work suggested data aggregator is used in between the mapper and reducer to enhance the performance of MapReduce. Initially the massive amount of data is partitioned into number of subset of data through the n number of independent mappers and it produces key value pairs for each partitioned data. Then the key value pairs are fed into aggregator where the data from different mappers are combining with smaller amount than the input. Followed by data aggregation data de duplication is carried over then repartition the data based on content, computation and network aware of data. Finally reducer merges the data to produce the final output, the proposed Content, computation and Network Aware (CCNA) MapReducer is compared with the existing Content Aware (CA) MapReducer and Content, computation Aware (CCA) MapReducer.

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Section: A Backgroundmentioning

confidence: 99%

Improving Mapreduce Process By Introducing Aggregator Repartition Data for Big Data Analytics

Kumar¹,

Ramkumar²,

Shankar³

et al. 2019

IJEAT

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“…Richa Sharma, et.al proposed in this paper [8], a study on the various methods in field of medical data mining utilizing different classification and clustering techniques. This survey study reveals the importance of research in area of life debilitating disease diagnosis.…”

Section: Literature Reviewmentioning

confidence: 99%

Two-step Technique for Prediction Analysis using K-Means Clustering Algorithm

Saxena¹,

Kumar²,

Tewari³

2017

IJCA

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The technique that is utilized for analyzing the complex data is known as data mining technique. As per the input dataset provided, the predictions are made for the data with the help of prediction analysis method. There are various new techniques proposed for the execution of prediction analysis technique. In this paper, the k-mean algorithm is utilized for categorizing the data. Further, for the classification of this data, the SVM classifier is applied. For improving the performance of prediction analysis in terms of accuracy the back propagation algorithm is used along with the k-mean clustering algorithm. For executing this proposed technique, the MATLAB tool is used. As per the experimental results it is concluded that the accuracy of the clustering algorithm is improved as well as the execution time utilized for prediction analysis is decreased.

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“…Lastly, a review of sources that describe the application of the different data mining techniques in the medical field is presented in [21] to identify useful classification and clustering approaches for the development of prediction systems. Also, the available data processing and classification tools are discussed and it is explained that, for pattern recognition, the choice of mining tasks depends on the characteristics of 4 Scientific Programming the data.…”

Section: Classification Techniquesmentioning

confidence: 99%

“…Nevertheless, to the best of our knowledge, there are not works which used association rule mining and Bayesian networks to analyze the decrease in the number of autopsies performed in a hospital; therefore Scientific Programming 5 [12] Bayesian networks Classification [13] Logistic regression, NB Classification [14] Re-RX, J48graft Classification [15] NB, SVM Classification [16] NB, SVM, RF Classification [17] J48, RF, KNN, NB, SVM Classification [18] NB, SVM, logistic regression, RF Classification [19] NB, OTM, InterVA-4 Classification [21] Decision tree, Neural Networks Classification [22] Association rules Association [23] Apriori Association [24] Fuzzy association rules Mining and fuzzy logic Association [25] Formal Concept Analysis Association…”

Section: Association Rule Mining Given the Variety Of Traditionalmentioning

confidence: 99%

Analysis of Medical Opinions about the Nonrealization of Autopsies in a Mexican Hospital Using Association Rules and Bayesian Networks

Delgado

Rodríguez-Mazahua

Guzmán³

et al. 2018

Scientific Programming

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This research identifies the factors influencing the reduction of autopsies in a hospital of Veracruz. The study is based on the application of data mining techniques such as association rules and Bayesian networks in data sets obtained from opinions of physicians. We analyzed, for the exploration and extraction of the knowledge, algorithms like Apriori, FPGrowth, PredictiveApriori, Tertius, J48, NaiveBayes, MultilayerPerceptron, and BayesNet, all of them provided by the API of WEKA. To generate mining models and present the new knowledge in natural language, we also developed a web application. The results presented in this study are those obtained from the best-evaluated algorithms, which have been validated by specialists in the field of pathology.

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Medical Data Mining Using Different Classification and Clustering Techniques: A Critical Survey

Cited by 21 publications

References 11 publications

Improving Mapreduce Process By Introducing Aggregator Repartition Data for Big Data Analytics

Improving Mapreduce Process By Introducing Aggregator Repartition Data for Big Data Analytics

Two-step Technique for Prediction Analysis using K-Means Clustering Algorithm

Analysis of Medical Opinions about the Nonrealization of Autopsies in a Mexican Hospital Using Association Rules and Bayesian Networks

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