Comparative Review on the Machine Learning Algorithms for Medical Data

The ML algorithm is subset of Artificial Intelligence which uses precise, probabilistic, and modernized method to extract data from a large and complicated data collection. Everyday hospitals are generating the huge amount of the medical data, reduction, prediction, classification and storing of this data is the biggest issue. This paper concentrates on the types of the medical data and some frequently used ML algorithms(i.e DT(Decision tree), SVM (Support Vector Machine), NB(Naïve Bayes), K-means, principal component analysis, Reinforcement). It also focuses on the comparative analysis of these algorithm with respect to its advantages, disadvantages and accuracy of predicting the data. From the result of comparision we can say that different ML algorithm along with the PCA gives better accuracy compared to the single classifier.