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Abstract
There have been many lethal diseases around the world causing various health issues that are getting very difficult for both prognosis and diagnosis of it. Nowadays, machine learning has become very popular in the health industry. As the amount of data is very high in volume and required a proper extraction to build a good predictive model, data mining is used extensively for this reason. The parameters can be chosen based on common symptoms. The frames need to be set well before a model is built. In this paper, we have applied various data mining techniques to a proposed predictive model to compare the accuracy of different algorithms. The experiment shows that logistic algorithms have an accuracy of 82.35% that is higher than other classifiers i.e. Support Vector Machine (SVM), Naive Bayes, Decision tree and K-nearest neighbour.