Automatic Tool for Prediction of Type of Cancer Risk and Recommendations

Authors(3) :-Pallavi Mirajkar, Dr. G. Prasanna Lakshmi, Dr. Ritu Khanna

Cancer can begin in any part of the body and can spread to other parts also. It is uncontrollable and it has many types. In the proposed thesis research paper, a tool for prediction of type of cancer risk with five different cancer diagnosis and recommendations is presented. For recognizing cancer disease number of tests ought to be required from the patient. But using data mining techniques these test can be diminished. Indeed, an accurate prediction of cancer is very difficult task for medical practitioner and it is also high concern to the patients so that better treatment can be given and it will also increase the survival time of the patients. Our findings suggested that suitable prediction tool can effectively reduce the several tests for diagnosing cancer and prediction accuracy thereby increasing the technical possibility of early detection of cancer. The main features of the tool comprise a balance between the number of necessary inputs and prediction performance, being portable, and it empowers the automatic development of the cancer risk prediction tool in cancer disease.

Authors and Affiliations

Pallavi Mirajkar
Research Scholar, Faculty of Computer Science, Pacific Academy of Higher Education and Research University, Udaipur, Rajasthan, India
Dr. G. Prasanna Lakshmi
(WOS-A) Andhra University, AU North Campus, Visakhapatnam, Andhra Pradesh, India
Dr. Ritu Khanna
Professor & Head, Basic Science, Faculty of Engineering, Pacific University, Udaipur, Rajasthan, India

Prediction Tool, Cancer, Data Mining, Automation, Integration.

  1. Fan Wang, Zachariah Dohogne , Jin Yang , Yu Liu and Benjamin Soibam “Predictors of breast cancer cell types and their prognostic power in breast cancer patients” BMC Genomics (2018) 19:137
  2. Dave Smith, “Data Mining in the Clinical Research Environment”. Available at.
  3. Damtew A., “Designing a predictive model for heart disease detection using data mining Techniques” A Thesis Submitted to the School of Graduate Studies of Addis Ababa University, 2011.
  5. Subrata Kumar Mandal “Performance Analysis Of Data Mining Algorithms For Breast Cancer Cell Detection Using Naïve Bayes, Logistic Regression and Decision Tree” International Journal Of Engineering And Computer Science ISSN: 2319-7242 Volume 6 Issue 2 Feb. 2017, Page No. 20388-20391.
  6. R. Kaviarasi, Dr. A.Valarmathi “Near The Beginning of Non Small Cell Lung Cancer Avoidance in Human Way of Life Risk Factors Classification Using Clustering Algorithm in the R Environment” International Journal of Advanced Research in Computer Science, 8 (5), May-June 2017,1023-1026.
  7. Arpit Bansal, Mayur Sharma “Improved k-mean clustering algorithm for prediction analysis using classification technique in data mining”, IJCA (0975 – 8887) Vol. 157 – No 6, January 2017.
  8. Prabhakar Chalise, Devin C.Koestler, Milan Bimali, Qing Yu, Brooke L.Fridley to “Integrative clustering methods for high –dimensional molecular data” , Transl Cancer Res 2014.
  9. Ahmad LG, Eshlaghy AT, Poorebrahimi A, Ebrahimi M, Razavi AR (2013) “Using Three Machine Learning Techniques for Predicting Breast Cancer Recurrence”. J Health Med Inform 4: 124. doi:10.4172/2157-7420.1000124
  10. V.Kirubha , S.Manju Priya “Survey on Data Mining Algorithms in Disease Prediction” International Journal of Computer Trends and Technology (IJCTT) – Volume 38 Number 3 - August 2016.
  11. P. Saranya , B. Satheeskumar “A Survey on Feature Selection of Cancer Disease Using Data Mining Techniques” International Journal of Computer Science and Mobile Computing, Vol.5 Issue.5, May- 2016, pg. 713-719.
  12. R.Senkamalavalli and Dr.T.Bhuvaneswari “Improved Classification Of Breast Cancer Data Using Hybrid Techniques” International Journal of Advanced Research in Computer Science Volume 8, No. 8, September-October 2017 ISSN No. 0976-5697.
  13. Sumalatha.G, Archana.S “A Study on Early Prevention and Detection of Breast Cancer using Data Mining Techniques” International Journal of Innovative Research in Computer and Communication Engineering (An ISO 3297: 2007 Certified Organization) Website: Vol. 5, Issue 6, June 2017.
  14. Megha Rathi , Vikas Pareek “Disease prediction tool: an integrated hybrid data mining approach for healthcare” IRACST - International Journal of Computer Science and Information Technology & Security (IJCSITS), ISSN: 2249-9555 Vol.6, No.6, Nov-Dec 2016.
  15. Rahul Patil, Pavan Chopade, Abhishek Mishra, Bhushan Sane, Yuvraj Sargar “Disease Prediction System using Data Mining Hybrid Approach” Communications on Applied Electronics (CAE) – ISSN : 2394-4714 Foundation of Computer Science FCS, New York, USA Volume 4 – No.9, April 2016.
  16. Tanu Minhas , Nancy Sehgal “Prediction Analysis Technique using Hybrid Clustering and SVM Classification” International Journal of Innovative Research in Science, Engineering and Technology (An ISO 3297: 2007 Certified Organization) Website: Vol. 6, Issue 7, July 2017.
  17. K. Suneetha “Early Prediction and Detection of Lung Cancer using Data Mining” International Journal of Advanced in Management, Technology and Engineering Sciences Volume 7, Issue 12, 2017 ISSN NO : 2249-7455.
  18. Chih-Jen Tseng, Chi-Jie Lu, Chi-Chang Chang, Gin-Den Chen, Chalong Cheewakriangkrai “Integration of data mining classification techniques and ensemble learning to identify risk factors and diagnose ovarian cancer recurrence” Contents lists available at Science Direct Artificial Intelligence in Medicine journal homepage: Artificial Intelligence in Medicine 78 (2017) 47–54.
  19. Huang M-W, Chen C-W, Lin W-C, Ke S-W, Tsai C-F (2017) “ SVM and SVM Ensembles in Breast Cancer Prediction” PLoS ONE 12(1):e0161501. doi:10.1371/journal.pone.0161501.
  20. Florije Ismaili , Luzana Shabani , Bujar Raufi , Jaumin Ajdari , Xhemal Zenuni “Enhancing breast cancer detection using data mining classification techniques” 2nd World Conference on Technology, Innovation and Entrepreneurship May 12- 14, 2017.
  21. Ana Silva, Tiago Oliveira, Vicente Julian, Jose Neves, and Paulo Novais “A Mobile and Evolving Tool to Predict Colorectal Cancer Survivability” Published by Springer International Publishing Switzerland 2016. All Rights Reserved L. Iliadis and I. Maglogiannis (Eds.): AIAI 2016, IFIP AICT 475, pp. 14–26, 2016.

Publication Details

Published in : Volume 5 | Issue 1 | January-February 2019
Date of Publication : 2019-01-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 01-08
Manuscript Number : CSEIT1838116
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Pallavi Mirajkar, Dr. G. Prasanna Lakshmi, Dr. Ritu Khanna, "Automatic Tool for Prediction of Type of Cancer Risk and Recommendations", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 1, pp.01-08, January-February-2019. Available at doi :
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