PRIOR PREDICTION AND IMPEDIMENT OF CANCER USING MACHINE LEARNING PROCESS
DOI:
https://doi.org/10.61841/n9se0j60Keywords:
Decision Maker Tree, K-Means Control, Prediction Chart, Clustering, Risk Control LevelsAbstract
In today’s life rapid changes occur drastically in climatic, food habits and heredity of gene mutations. Human health factor is causing the growth of death rate worldwide. Among the various dead causes, caner is the leading disease worldwide. Early prevention and detection of cancer will play a vital role in decreasing the deaths caused by cancer disease. Present work proposes a hybrid method of multi-layered which combines decision tree classification and clustering technique for building a prediction and cancer risk system. The proposed work will predict, breast, lungs, cervix, oral, blood and stomach cancers and is a user interface system which reduces cost and time saving. The present work is based on machine learning method which uses clustering, classification and prediction in identifying the probability of cancer in human being. The datasets collected are preprocess, feeded to the classifier which yields related patterns based on decision making algorithm.The data generated is clustered using k-means algorithm of clustering in separating non-cancer with that of cancer patient. Further it is found that the clusters of cancer are divided into 6 categories. The early prediction system developed analyzed various levels of risk and helped in diagnosis.
Present research help in detection the predisposition of person early canceric conditions.
Downloads
References
1. Sridevi “An shrewd Classifier for Breast melanoma diagnosis established on okay-method Clustering and hard Set” worldwide Journal of laptop functions
2. ok.Vani “Childhood melanoma-a sanatorium situated study utilizing choice Tree strategies” Journal of computer Science 7(12): 1819-1823, 2011 ISSN: 1549-363
3. Revathi “A Survey on data Mining utilizing Clustering methods” global Journal of Scientific & Engineering research
4. Chaurasia “information Mining tactics: to predict and get to the bottom of Breast cancer Survivability” international journal of computer Science and cell Computing (IJCSMC).
5. Yadav “Chemotheraphy Prediction of cancer patient via making use of knowledge Mining approaches” international Journal of pc functions
6. M. Durairaj “data Mining functions in HealthcareSector: A be taught” global journal of Scientific &technology study, volume 2, limitation 10.
7. Khan “Classification and Regression analysis of the Prognostic Breast cancer utilising iteration Optimizing Algorithms” global Journal of computer applications (0975-8887)
8. Rajkumar “ intelligent pattern Mining and knowledgeClustering for sample Cluster analysis making use of melanomaknowledge” global journal of Engineering Science andscience.
9. Boris Milovic “Prediction and decision Making in well being Care making use of knowledge Mining” global Journal of Public well being Science Vol. 1.
10. Padhy “The Survey of data Mining functions and characteristic Scope” Asian Journal of computer Science and knowledge science 2:4(2012).
11. S. Kumar “progress of an efficient Clustering technique for Colon Dataset” worldwide Journal of Engineering and revolutionary technology” quantity 1, quandary 5, may 2012 ISSN: 2277-3754
12. Gracia Jacob “information Mining in medical data sets: A. Review” global Journals of utilized knowledge system (IJAIS)
13. M. Elsayad “prognosis of Breast melanoma using decisionmodels and SVM” worldwide Journal of pc applications
14. Balachandran “Classifiers centered process for Pre-diagnosis of Lung cancer sickness” global Journal of pc applications
15. Sahar “Predicting the Serverity of Breast plenty with data Mining ways” international Journal of computer Science problems, Vol. 10
16. k. Rajneet “making use of Some information Mining methods to Predict the Survival 12 months of Lung melanoma sufferer” global Journal of computer Science April 2013, pg.1 – 6, ISSN 2320–088X
17. Krishnaiah.V “prognosis of Lung cancer Predictionprocessutilising knowledge Mining Classification tactics”worldwide Journal of laptop Science
18. E. Charles “Comparative learn of knowledge Mining and Statistical finding out strategies for Prediction of cancer Survivability” Mediterranean journal of Social Sciences
19. Rajashree sprint “A hybridized ok-means clustering method for high dimensional dataset” globalJournal of Engineering, Science and technological know-how
20. Ritu Chauhan “data clustering system for discovering clusters in spatial melanoma databases” worldwide Journal of laptop purposes
21. Dechang Chen “establishing Prognostic techniques of cancer patients by means of Ensemble Clustering” Hindawipublishing enterprise, Journal of Biomedicine and Biotechnology volume 2009, Article id 632786.
22. Halawani “A study of digital mammograms by using utilizing clustering algorithms” Journal of Scientific & Industrialresearch Vol. 71, September 2012, pp. 594-600.
23. Zakariaubi “Improves remedy programs of Lung cancer utilizing information Mining methods” Journal ofsoftware Engineering and applications, February 2014, volume 7, sixty nine-77
24. Abdulgafoor “Detection of brain Tumor utilizing Modified ok-way Algorithm and SVM” global Journal of laptop purposes (0975 – 8887)
25. Anton Syroeshkin, Olga Levitskaya, Elena Uspenskaya, Tatiana Pleteneva, Daria Romaykina, Daria Ermakova. "Deuterium Depleted Water as an Adjuvant in Treatment of Cancer." Systematic Reviews in Pharmacy 10.1 (2019), 112-117. Print. doi:10.5530/srp.2019.1.19
26. Haas, A.S., Langer, E.J.Mindful attraction and synchronization: Mindfulness and regulation of interpersonal synchronicity(2014) NeuroQuantology, 12 (1), pp. 21-34.
27. Stapp, H.P.Reply to a critic: "Mind efforts, quantum Zeno effect and environmental decoherence"(2012) NeuroQuantology, 10 (4), pp. 601-605.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
