چکیده مقاله
One of the applications of data mining is in medicine and model construction for disease diagnosis The more the modellearns from previous data, the more accurate it would perform The essential issue is that, the training and testing data in classificationof data must be selected in a way that the model enjoys the most efficient learning from previous data and the highest accuracy indiagnosis of the disease In this study, the Pima dataset of diabetics is applied, the models for predicting and diagnosing diabetes aredeveloped based on KNN, SVM, Nave Bayesian and Decision Tree classification methods and the accuracy of each model is evaluated The effectiveness of k fold validation on the accuracy of each model is assessed According to the findings here, k fold cross validationincreases the model accuracy and a classification technique would not always have the best performance and accuracy, while it dependson the nature and complexity of the dataset The simulation is made by the tool named RapidMiner
کلیدواژهها
نویسندگان
Nasim Nikbakhsh
Department of computer, Isfahan khorasgan Branch Islamic Azad University Isfahan, Iran
GholamReza Dehghani
Department of computer, Isfahan khorasgan Branch Islamic Azad University Isfahan, Iran
Farsad Dr.Zamani
Department of computer, Isfahan khorasgan Branch Islamic Azad University Isfahan, Iran
شیوه ارجاع
Nikbakhsh, Nasim and Dehghani, GholamReza and Dr.Zamani, Farsad,1395,Comparing classification algorithms of data mining in diagnosis of diabetes and assessing the effectiveness of k-fold cross validation in the accuracy of the constructed model, International Conference on Engineering and Computer Science,Najafabad
ارائهشده در
مجموعه مقالات کنفرانس بین المللی مهندسی و علوم کامپیوتر3 اسفند 1395 · نجف آباد