Call for Paper - January 2023 Edition
IJCA solicits original research papers for the January 2023 Edition. Last date of manuscript submission is December 20, 2022. Read More

Astrological Prediction for Profession Doctor using Classification Techniques of Artificial Intelligence

International Journal of Computer Applications
© 2015 by IJCA Journal
Volume 122 - Number 15
Year of Publication: 2015
Neelam Chaplot
Praveen Dhyani
O. P. Rishi

Neelam Chaplot, Praveen Dhyani and O P Rishi. Article: Astrological Prediction for Profession Doctor using Classification Techniques of Artificial Intelligence. International Journal of Computer Applications 122(15):28-31, July 2015. Full text available. BibTeX

	author = {Neelam Chaplot and Praveen Dhyani and O. P. Rishi},
	title = {Article: Astrological Prediction for Profession Doctor using Classification Techniques of Artificial Intelligence},
	journal = {International Journal of Computer Applications},
	year = {2015},
	volume = {122},
	number = {15},
	pages = {28-31},
	month = {July},
	note = {Full text available}


Astrology can be an interesting example of the application of various classification techniques of artificial intelligence. In astrology, predictions about different aspects of human life are done based on the planetary position of the stars at the time of birth of a person. In this research work, the positions of the planets and stars at the time of the birth of a person are utilized. This information is used to predict the possibility of person to become doctor. Total 102 records were collected for the study out of that half of the records were of persons that were doctor and other half records of the persons that were not doctor by profession. Thereafter, various supervised classification techniques such as Logistic, Naïve Bayes, Simple Cart, Decision Stump, Decision Table and DTNB algorithm were used and results were compared for their accuracy.


  • Hall, M. , Frank, E. , Holmes G. , Pfahringer, B. , Reutemann, P. , & Witten, I. H. 2009. The WEKA data mining software. ACM SIGKDD Explorations Newsletter.
  • Ivan W. Kelly. 1997. A Concept of Modern Astrology a Critique. Article in Psychological Reports.
  • John H. Mcgrew and Richard M. Mcfall. 1990. A Scientific Inquiry Into the Validity of Astrology. Journal of Scientific Exploration.
  • Ken McRitchie. August 2011. Support for Astrology from the Carlson Double-blind Experiment. ISAR International Astrologer.
  • Penny Seator. 2008/2009 Astrological Prediction and Statistical Tests. The British Astrological Association.
  • S. B. Kotsiantis, I. D. Zaharakis, P. E. Pintelas. 2007. Machine learning: a review of classification and combining techniques. Springer Science Business Media
  • B Folorunsho Olaiya, Adesesan Barnabas Adeyemo. 2012. Application of Data Mining Techniques in Weather Prediction and Climate Change Studies. I. J. Information Engineering and Electronic Business.
  • Joseph A. Cruz and David S. Wishart. 2006. Applications of Machine Learning in cancer prediction and prognosis cancer. Informatica
  • Harold Somers. June 1999. Review Article: Example-based Machine Translation. Machine Translation.
  • Neelam Chaplot, Praveen Dhyani, O. P. Rishi. March 2013. A Review on Machine Learning Concepts for Prediction Based Application, International Journal of Computational Science, Engineering & Technology.
  • O. P. Rishi and Neelam Chaplot. Dec 2010. Predictive role of case based reasoning for astrological predictions about profession: System modeling approach, International Conference on Communication and Computational Intelligence.
  • O. P. Rishi and Neelam Chaplot. Dec 2010. Archetype of astrological prediction system about profession of any persons' using case based reasoning, in International Conference on Communication and Computational Intelligence.
  • le Cessie S. , Van Houw ngen J. C. 1992. Ridge Estimators in Logistic Regression. Applied Statistics.
  • Leo Breiman, Jerome H. Friedman, Richard A. Olshen, Charles J. Stone. 1984. Classification and Regression Trees. Wadsworth International Group, Belmont, California.
  • Harry Zhang, Jiang Su. 2004. Naive Bayesian Classifiers for Ranking. Lecture Notes in Computer Science Volume 3201.
  • Ron Kohavi and Daniel Sommer. 1998. Targeting Business Users with Decision Table Classifiers. KDD-98 Proceedings American Association of Artificial Intelligence.
  • Kohavi R, 1995. The Power of Decision Tables. In Proceeding European Conference on Machine Learning.
  • Iba, Wayne and Langley, Pat. July 1992. Induction of One-Level Decision Trees. ML92: Proceedings of the Ninth International Conference on Machine Learning, Aberdeen, Scotland, San Francisco, CA, Morgan Kaufmann.
  • Mark Hall, Eibe Frank. 2008. Combining Naive Bayes and Decision Tables. In Proceedings of the 21st Florida Artificial Intelligence Society Conference.