Estimating diabetes risk using Nave Bayes classifiers

Abstract

Diabetes is now a common disease for modern people. Diabetes will cause some serious symptoms. Diabetes patients will face a painful life, high cure cost, and even death. So, it is necessary to correctly diagnose diabetes and analyze the factors that mainly cause diabetes to prevent the happen of diabetes. This essay mainly focuses on training the computer to help the doctor to train the computer. Three Naive Bayes classifications will be used to train the computer to do the prediction, including the Gaussian Nave Bayes, Bernoulli Nave Bayes, and Multinomial Nave Bayes. To compare each methods result, accuracy will be the main index to measure whether a method is good enough to put into use. Not only the accuracy, classification report, and confusion matrix also assist to measure the prediction. Finally, the Gaussian Nave Bayes has the highest accuracy and when combined with the confusion matrix and the classification report, the Gaussian Nave Bayes has a huge advantage over the other two models. The accuracy of these models still does not satisfy medical demand. Some deep learning and high-level model are expected to optimize this project.