Predicting Diabetes by adopting Classification Approach in Data Mining
As the world is growing fast, the metamorphosing of things, lifestyle, perceptions of people and resources is taking place. But the elevation in technology has become a challenge now as the ideas, innovations are amplifying. One of the biggest things the advancement and elevations in technology has...
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| Format: | EJournal Article |
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Politeknik Negeri Padang,
2019-08-10.
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| Online Access: | Get Fulltext |
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| LEADER | 02324 am a22002653u 4500 | ||
|---|---|---|---|
| 001 | IJOIV_229_209 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Kaur, Rapinder |e author |
| 100 | 1 | 0 | |e contributor |
| 245 | 0 | 0 | |a Predicting Diabetes by adopting Classification Approach in Data Mining |
| 260 | |b Politeknik Negeri Padang, |c 2019-08-10. | ||
| 500 | |a https://joiv.org/index.php/joiv/article/view/229 | ||
| 520 | |a As the world is growing fast, the metamorphosing of things, lifestyle, perceptions of people and resources is taking place. But the elevation in technology has become a challenge now as the ideas, innovations are amplifying. One of the biggest things the advancement and elevations in technology has given birth is "Big Data". In this data massive amount of information is hidden. In order to refine or process this data and to find out and unmask the insights, many techniques and algorithms have been evolved, one of which is the data mining. The data mining is the approach or procedure which helps in detaching or extracting profitable and fruitful knowledge, reports and facts from the rough or impure data. The prediction analysis is approach comprehended from data mining to forecast and figure out the future making using classification technique. This research work is based on the diabetes prediction by making use of classification approach. In the existing approach SVM classifier is applied for the prediction analysis. To increase accuracy approach of KNN classifier is applied for the prediction analysis. Both the proposed and existing methods are implemented in Python. The simulation results show that accuracy of KNN is increased and execution time is reduced. | ||
| 540 | |a Copyright (c) 2019 Rapinder Kaur | ||
| 540 | |a http://creativecommons.org/licenses/by-sa/4.0 | ||
| 546 | |a eng | ||
| 690 | |a Diabetes, SVM, KNN | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |a Peer-reviewed Article |2 local | |
| 786 | 0 | |n JOIV : International Journal on Informatics Visualization; Vol 3, No 2-2 (2019): Internet of Things and Smart Environments; 218 - 221 | |
| 786 | 0 | |n 2549-9904 | |
| 786 | 0 | |n 2549-9610 | |
| 787 | 0 | |n https://joiv.org/index.php/joiv/article/view/229/209 | |
| 856 | 4 | 1 | |u https://joiv.org/index.php/joiv/article/view/229/209 |z Get Fulltext |