Survey on Predictive Analysis of Diabetes Disease Using Machine Learning Algorithms

Big data analysis is predicated on large amount of data. Diabetes is caused due to the excessive amount of sugar condensed into the blood. One of the most critical chronic healthcare problems is diabetes.Undiagonosed diabetes problem may leads to damage eyes, heart, kidneys and nerves of diabetes patients. If improper medication taken is done which also lead to death. Early detection of diabetes is very important to maintain healthy life. Machine learning algorithm to identify a best predicting algorithm based various matrices such us accuracy, precision, recall, F-measure, sensitivity and specificity. This paper discusses about various ML techniques to predict the Diabetes disease by using dataset .Machine learning algorithm namely Decision Tree, SVM, Naive Bayes, Random forest, k-NN, K-mean clustering and LR algorithms are used in their experiment to detect diabetes at an early stage. Experiments are performed on Pima Indian diabetes dataset (PIDD) which is sourced from UCI Machine Learning repository. Result obtained show SVM outperform with high accuracy of 82% comparatively other algorithms.