• Mohnish Mahamune
• Pramod Deo

In this paper we review the current research being utilized using the data mining methods for the analysis and forecast diagnosis of numerous diseases, underlining critical concerns, and reviewing the methods in a set of cultured practices. The motto of this review is to classify and evaluate the most frequently used algorithms of data mining on healthcare databases. The following algorithms have been known as applications of data mining in healthcare: Decision Trees (DT’s) C4.5 and C5, Support Vector Machine (SVM), artificial neural networks (ANNs) and their Multilayer Perceptron model, Naïve Bayes, Logistic Regression, Genetic Algorithms (GAs) / Evolutionary Programming (EP), Fuzzy Rules. Review presents that it’s challenging to claim one algorithm of data mining as the best appropriate for the healthcare diagnosis of diseases. At present certain algorithms achieve better than others, but there are some cases when a pipeline of the facts for said algorithms claims outcomes more operational.

Data Mining, Knowledge Management, Healthcare Data Mining, Data Mining review

"Healthcare Data Mining Literature Review 2012 to 2019", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.2997-3002, May-2019, Available :http://www.jetir.org/papers/JETIR1905V16.pdf

"Healthcare Data Mining Literature Review 2012 to 2019", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp2997-3002, May-2019, Available at : http://www.jetir.org/papers/JETIR1905V16.pdf