There has been an exponential growth in the number of cardiovascular diseases and cancer in the present world due to unfavorable environmental factors, faulty food, stress and erroneous lifestyle. These two account for a majority of deaths worldwide. Early detection and prevention plays a remarkable role in preventing deaths. It is not an easy task for medical practitioners to instantly come to a conclusive diagnosis. Hence we can resort to data mining techniques to extract occult, foreseeable information that can be acted upon the large set of medical data. In this survey, we have presented an overview on the symptoms, their aggravating factors in various cardiac illnesses and cancer. We have also enlisted, discussed and analyzed data mining techniques such as Decision Tree, Neural Networks, and Naïve Bayes etc. This paper summarizes various review and technical journals on cardiovascular disease and cancer diagnosis and prognosis

Cardiovascular diseases, Cancer, Diagnosis, Prognosis, Data Mining

[1] D. Tomar, S. Agarwal, “A survey on Data Mining approaches for Healthcare”, International Journal of Bio-Science and Bio-Technology, Vol 5(5),pp.241-266, 2013.
[2] K. Park , “Park’s Textbook of PREVENTIVE AND SOCIAL MEDICINE”, 23rd Edition, Bhanot publishers, India, pp 363-385,2015.
[3] Colin Ogilvie, Christopher C. Evans, “An Introduction to Medical Diagnosis”, 11th Edition,Butterworth-Heinemann, Oxford, pp 201-205.
[4] S.Vijayarani, S.Sudha, “A Study of Heart Disease Prediction in Data Mining” International Journal of Computer Science and Information Technology & Security “, (IJCSITS), ISSN: 2249-9555 Vol. 2, No.5, October 2012 .
[5] M. Hejmadi, “Introduction to Cancer Biology”,ebook 2nd Edition, Bookboon publishing, pp- 7, 2010, ISBN 978-87-7681-478-6
[6] J.F. McCARTHY, K.A. Marx, P.E.Hoffman, A.G. GEE, P O’neil, M.L.Ujwal, and J Hotchkiss, "Applications of Machine Learning and High‐Dimensional Visualization in Cancer Detection, Diagnosis, and Management." Annals of the New York Academy of Sciences1020, no. 1 (2004): pp. 239-262.
[7] Mahajan & Gupta, “Textbook of PREVENTIVE AND SOCIAL MEDICINE”,4th Edition,Jaypee brothers medical publishers, India, PP 359,2013, ISBN 978-93-5090-187-8 978-
[8] S. Palaniappan, R. Awang, “Intelligent heart disease prediction system using data mining techniques”, IJCSNS International Journal of Computer Science and Network Security, VOL.8 No.8, 343-349,August 2008
[9] N. Guru, A. Dahiya, N. Rajpal, "Decision Support System for Heart Disease Diagnosis Using Neural Network", Delhi Business Review, Vol. 8, No. 1 (January - June 2007).
[10] Ramyasri M M (14CSR151), Renuka P (14CSR158) and Rajeshkumar R (14CSL275) and Kumaravel T, “Prediction of Heart Diseases Risk through Frequent Itemsets in Data Mining “3 rd National Conference on Innovative Research Trends in Computer Science and Technology (NCIRCST) ISSN: 2454-4248 Volume: 4 Issue: 3 115 – 120, 2018
[11] I. U. Said , A. H. Adam , Dr. A. B. Garko, “ASSOCIATION RULE MINING ON MEDICAL DATA TO PREDICT HEART DISEASE”, International Journal Science Technology and Managaement Vol.No.4, Issue 08, Aug 2015
[12] D. C.Sekar,& K. R. H.Rao, Predicting the heart attack symptoms using biomedical data mining techniques. International Journal of Computer Science & Applications (TIJCSA), 1(3),(2012).
[13] S. M. Alzahani, A. Althopity, A. Alghamdi, B. Alshehri, & S. Aljuaid, (2014). An overview of data mining techniques applied for heart disease diagnosis and prediction. Lecture Notes on Information Theory Vol, 2(4),December 2014
[14] V. Chaurasia , S. Pal, “A Novel Approach For Breast Cancer Detection Using Data Mining Techniques” , International Journal Of Innovative Research in Computer and Communication Engineering (An ISO 3297: 2007 Certified Organization) Vol. 2, Issue 1, 2014
[15] D. Delen, G. Walker, A. Kadam, “Predicting Breast Cancer Survivability: A Comparison Of Three Data Mining Methods”, Elsevier: Artificial Intelligence in Medicine , Vol. 34, Issue 2, pp: 113-127, 2005
[16] A Bellaachia & E. Guven, “Predicting Breast Cancer Survivability Using Data Mining Techniques”, Age, Vol. 58, Issue 13, pp: 10-110, 2006
[17] M. Karabatak & M. C. Ince, “An Expert System For Detection Of Breast Cancer Based On Association Rules And Neural Network”, Elsevier: Expert Systems With Applications, Vol. 36, Issue 2, Part 2, pp: 3465-3469, 2009
[18] N. Sharma and H. Om, “Framework For Early Detection And Prevention Of Oral Cancer Using Data Mining”, International Journal of Advances in Engineering & Technology, Vol. 4, Issue 2, pp: 302-310, 2012.
[19] C. Scully, J.V. Bagan, C. Hopper, J.B. Epstein, “Oral Cancer: Current And Future DiagnosticsTechniques – A Review Article”, American Journal of Dentistry, Vol. 21, Issue 4, pp: 199-209, 2008
[20] W. T. Tseng, W. F. Chiang, S. Y. Liu, J. Roan, & C. N. Lin, “The Application Of DataMining Techniques To Oral Cancer Prognosis”, Journal Of Medical Systems, Vol.39, Issue 5, pp: 59,2015
[21] Gadewal & Zingde, “Database And Interaction Network Of Genes Involved In Oral Cancer: Version II”, Online Journal of Bioinformatics, Vol. 6, Issue 4, pp:169-170, 2011
[22] D. Kaladhar, B. Chandana and P. B. Kumar, “Predicting Cancer Survivability Using Classification Algorithms”, International Journal Of Research And Reviews In Computer Science (IJRRCS), Vol 2, Issue 2, pp: 340 – 343, 2011
[23] V.Krishnaiah, Dr.G.Narsimha, Dr.N.Subhash Chandra,“Diagnosis of lung cancer prediction system using data mining classification techniques”. International Journal of Computer Science and Information Technologies, Vol. 4, Issue 1, pp.39-45, 2013.
[24] S. Patel and H. Patel, “Survey of data Mining techniques used in healthcare Domain”, International Journal of Information Sciences and Techniques, Vol. 6,pp.53-60 , 2016.
[25] National Library of Australia Cataloguing–in–Publication data: Lifestyle and cancer: knowledge, attitudes and behavior in NSW 2009 SHPN (CI) 120203, Published by the Cancer Institute NSW, pp.1-29, 2012, ISBN 978-1-74187-760-1
[26] K. Ahmed, T.Jesmin, & M.Z. Rahman,“Early prevention and detection of skin cancer risk using data mining”, International Journal of Computer Applications, Vol. 62, Issue 4,pp.1-6, 2013.
[27] D. Burdick, M. Calimlim and J. Gehrke, “MAFIA: A Maximal Frequent Itemset Algorithm for Transactional Databases”, Proceedings of the 17th International Conference on Data Engineering, pp.443-452, April 02-06, 2001.
[28] D. Burdick, M. Calimlim, J. Flannick, J. Gehrke and T. Yiu, MAFIA: “A Performance Study of Mining Maximal Frequent Itemsets”, Proceedings of the 17th International Conference on Data Engineering, pp.443-452, April 02-06, 2001.
[29] E.I.Papageorgiou, P.P.Spyridonos, D. Th.Glotsos, C.D.Stylios, P.Ravazoula, G.N.Nikiforidis, P.P.Groumpo, “Brain Tumor Characterization Using The Soft Computing Technique Of Fuzzy Cognitive Maps, Elsevier: Applied Soft Computing, Vol. 8, Issue 1, pp: 820-828, 2008.
[30] R.S.Santos, S.M.F.Malheiros, S.Cavalheiro, J.M.P. Oliveira,” A Data Mining System For Providing Analytical Information On Brain Tumors To Public Health Decision Makers, Elsevier: Computer Methods And Programs In Biomedicine, Vol. 109, Issue 3, pp: 269-282, 2013
[31] E. I. Papageorgiou, P. P. Spyridonos, D. T. Glotsos, C. D. Stylios, P. Ravazoula, G. N. Nikiforidis, & P. P. Groumpos, Brain Tumor Characterization Using The Soft Computing Technique Of Fuzzy Cognitive Maps, Elsevier:Applied Soft Computing, Vol. 8, Issue 1, pp: 820-828, 2008