Prediction of Breast Cancer Using Extremely Randomized Clustering Forests (ERCF) Technique: Prediction of Breast Cancer
Breast cancer is a significant public health concern in both developed and developing countries. It is almost one in three cancers diagnosed in all women. Data mining and pattern recognition applications in conjunction have been proven to be quite useful and relevant to extract the information usefu...
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Veröffentlicht in: | International journal of distributed systems and technologies 2021-10, Vol.12 (4), p.1-15 |
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creator | Wairya, Subodh Gupta, Akhil Anand, Rohit Pandey, Digvijay Sindhwani, Nidhi Pandey, Binay Kumar Sharma, Manvinder |
description | Breast cancer is a significant public health concern in both developed and developing countries. It is almost one in three cancers diagnosed in all women. Data mining and pattern recognition applications in conjunction have been proven to be quite useful and relevant to extract the information useful for the medical purpose. This research work reflects the work based on extremely randomized clustering forests (ERCF) technique which is nothing but a type of pattern recognition technique that may be implemented as the prediction model for breast cancer (BC). The accuracy achieved through ERCF has also been compared with that of k-NN (correlation) and k-NN (Euclidean) in this research work (where k-NN refers to k-nearest neighbours technique), and thereafter, final conclusions have been drawn depending upon the testing attributes. The results show that the accuracy of ERCF in the forecasting of breast cancer is so much larger than that of the exactness of k-NN (correlation) and k-NN (Euclidean). Hence, ERCF, a randomized technique for pattern classification, is best. |
doi_str_mv | 10.4018/IJDST.287859 |
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It is almost one in three cancers diagnosed in all women. Data mining and pattern recognition applications in conjunction have been proven to be quite useful and relevant to extract the information useful for the medical purpose. This research work reflects the work based on extremely randomized clustering forests (ERCF) technique which is nothing but a type of pattern recognition technique that may be implemented as the prediction model for breast cancer (BC). The accuracy achieved through ERCF has also been compared with that of k-NN (correlation) and k-NN (Euclidean) in this research work (where k-NN refers to k-nearest neighbours technique), and thereafter, final conclusions have been drawn depending upon the testing attributes. The results show that the accuracy of ERCF in the forecasting of breast cancer is so much larger than that of the exactness of k-NN (correlation) and k-NN (Euclidean). 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Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c345t-4139daf4fdabc563acc4a28bd11f200a11c9c3158443f1e3980ecb6ecdbbef8a3</cites><orcidid>0000-0002-4041-1213 ; 0000-0001-9158-0466 ; 0000-0003-0353-174X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/2931909333?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,780,784,21388,27924,27925,33744,43805,64385,64389,72469</link.rule.ids></links><search><creatorcontrib>Wairya, Subodh</creatorcontrib><creatorcontrib>Gupta, Akhil</creatorcontrib><creatorcontrib>Anand, Rohit</creatorcontrib><creatorcontrib>Pandey, Digvijay</creatorcontrib><creatorcontrib>Sindhwani, Nidhi</creatorcontrib><creatorcontrib>Pandey, Binay Kumar</creatorcontrib><creatorcontrib>Sharma, Manvinder</creatorcontrib><title>Prediction of Breast Cancer Using Extremely Randomized Clustering Forests (ERCF) Technique: Prediction of Breast Cancer</title><title>International journal of distributed systems and technologies</title><description>Breast cancer is a significant public health concern in both developed and developing countries. It is almost one in three cancers diagnosed in all women. Data mining and pattern recognition applications in conjunction have been proven to be quite useful and relevant to extract the information useful for the medical purpose. This research work reflects the work based on extremely randomized clustering forests (ERCF) technique which is nothing but a type of pattern recognition technique that may be implemented as the prediction model for breast cancer (BC). The accuracy achieved through ERCF has also been compared with that of k-NN (correlation) and k-NN (Euclidean) in this research work (where k-NN refers to k-nearest neighbours technique), and thereafter, final conclusions have been drawn depending upon the testing attributes. The results show that the accuracy of ERCF in the forecasting of breast cancer is so much larger than that of the exactness of k-NN (correlation) and k-NN (Euclidean). Hence, ERCF, a randomized technique for pattern classification, is best.</description><subject>Accuracy</subject><subject>Analysis</subject><subject>Breast cancer</subject><subject>Clustering</subject><subject>Data mining</subject><subject>Developing countries</subject><subject>Forests and forestry</subject><subject>India</subject><subject>LDCs</subject><subject>Methods</subject><subject>Pattern classification</subject><subject>Pattern recognition</subject><subject>Prediction models</subject><subject>Public health</subject><subject>Technology application</subject><issn>1947-3532</issn><issn>1947-3540</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>N95</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kUFP4zAQhSO0SCCWGz_AEheQtmDHThNz62bbXVAlELRny3HGqVEbsx5HC_z6DQQBh4o5zFjypzej95LkiNEzQVlxfnn1625xlhZ5kcmdZJ9JkY94Jui39zdP95JDxHvaVybyfCz3k383AWpnovMt8Zb8DKAxklK3BgJZomsbMn2MATawfiK3uq39xj1DTcp1hxHCy__MB8CI5GR6W85OyQLMqnV_O7ggX2h_T3atXiMcvs2DZDmbLso_o_n178tyMh8ZLrI4EozLWltha12ZbMy1MUKnRVUzZlNKNWNGGs6yQghuGXBZUDDVGExdVWALzQ-S40H3Ifj-Jozq3neh7VeqVHImqeR9vVONXoNyrfUxaLNxaNQkH1PK84JlPfXjE1V1vTuAfUPXrCI2ukPcipvgEQNY9RDcRocnxah6iUy9RqaGyHq8HHDXuI8jB_uUt2qwTw32qeU2DZby_8tBn6Q</recordid><startdate>20211001</startdate><enddate>20211001</enddate><creator>Wairya, Subodh</creator><creator>Gupta, Akhil</creator><creator>Anand, Rohit</creator><creator>Pandey, Digvijay</creator><creator>Sindhwani, Nidhi</creator><creator>Pandey, Binay Kumar</creator><creator>Sharma, Manvinder</creator><general>IGI Global</general><scope>AAYXX</scope><scope>CITATION</scope><scope>N95</scope><scope>7SC</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>L6V</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M7S</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><orcidid>https://orcid.org/0000-0002-4041-1213</orcidid><orcidid>https://orcid.org/0000-0001-9158-0466</orcidid><orcidid>https://orcid.org/0000-0003-0353-174X</orcidid></search><sort><creationdate>20211001</creationdate><title>Prediction of Breast Cancer Using Extremely Randomized Clustering Forests (ERCF) Technique: Prediction of Breast Cancer</title><author>Wairya, Subodh ; 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It is almost one in three cancers diagnosed in all women. Data mining and pattern recognition applications in conjunction have been proven to be quite useful and relevant to extract the information useful for the medical purpose. This research work reflects the work based on extremely randomized clustering forests (ERCF) technique which is nothing but a type of pattern recognition technique that may be implemented as the prediction model for breast cancer (BC). The accuracy achieved through ERCF has also been compared with that of k-NN (correlation) and k-NN (Euclidean) in this research work (where k-NN refers to k-nearest neighbours technique), and thereafter, final conclusions have been drawn depending upon the testing attributes. The results show that the accuracy of ERCF in the forecasting of breast cancer is so much larger than that of the exactness of k-NN (correlation) and k-NN (Euclidean). 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subjects | Accuracy Analysis Breast cancer Clustering Data mining Developing countries Forests and forestry India LDCs Methods Pattern classification Pattern recognition Prediction models Public health Technology application |
title | Prediction of Breast Cancer Using Extremely Randomized Clustering Forests (ERCF) Technique: Prediction of Breast Cancer |
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