Application of naïve bayes classification for disease prediction Mathur Sharad*, Dr. Joshi Bhavesh** *Research Scholar, Faculty of Computer Science, PAHER University, Udaipur (Rajastha), India **Research Guide, Faculty of Computer Science, PAHER University, Udaipur, (Rajastha), India Online published on 24 October, 2019. Abstract In today's era data mining is widely used for disease prediction in healthcare sector. Data mining is process of discovering information from large datasets or other repositories. It is a difficulty work to predict diseases from the available large medical dataset. To find solution of this problem researcher use apply various data mining technique. Classification is a method of data analysis that can be implemented for developing models to elaborate significant classes of data. One of the efficient and famous classification techniques of data mining is Naïve Bayesian (NB). Bayesian method is basically very significant and effective data mining technique. Providing the probability distribution, this classifier can provably obtain best result. It works on the basis of theory of probability. In this paper we applied Naïve Bayesian classifier on Asthma disease dataset and compared different Bayes Classifiers available in Weka data mining tool. We compared algorithms on the basis of three measures-Recall, Precision and F-Measure. Top Keywords Disease Prediction, Classification, Naïve Bayesian, Weka, Precision. Top |