Comparison of Machine Learning Algorithms in Data classification

Data Mining is used to extract the valuable information from raw data. The task of data mining is to utilize the historical data to discover hidden patterns that helpful for future decisions. To analyze the data machine learning classifiers are used. Various data mining approaches and machine learning classifiers are applied for prediction of diseases. Where can supports, in timely treatment. The aim of this work is to compare the performance of ML classifier. These ML classifiers are Logistic Regression, Decision Tree, Niven Bayes, k-Nearest Neighbors, Support Vector Machine and Random Forests classifiers on two datasets on the basis of its accuracy, precision and f measure. The experimental results reveal that it's found that the Random Forests performance is better than the other classifiers. It gives 83% accuracy in heart data sets and 85% accuracy in hepatitis disease prediction.