• Hiren R. Kavathiya
• Dr. G. C. Bhimani

The results of the present investigation with regard to the Data Mining learning models for five medical data sets viz., Bayes Network, Naïve Bayes, J48, Decision stump, Ripper, K-NN, Bagging and Dagging are presented and discussed. Different DM techniques are applied for analysing and testing the medical data and their potential benefits for diagnosis and learning are discussed. Uncertainty is present in most medical applications due to the vague logical rules and their imprecision and hence a good method called the rule representation from the decision tree method is used as it’s also easily understandable. An exact representation of the relationship between symptoms and diagnosis is given by the integration of fuzzy set and DM methods. In conclusion, early prediction of the diseases can be done looking at the results to increase the survival rates of the patients.

Fuzzy logic, machine learning, Data Mining,

"Analytics for Medical data Using Different Comparative learning Techniques ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 2, page no.1304-1308, February-2020, Available :http://www.jetir.org/papers/JETIR2002398.pdf

"Analytics for Medical data Using Different Comparative learning Techniques ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 2, page no. pp1304-1308, February-2020, Available at : http://www.jetir.org/papers/JETIR2002398.pdf