Comparative Performance Analysis of Datamining and Machine Learning Techniques for Diabetes Prediction
DOI:
https://doi.org/10.26438/ijcse/v11i7.17Keywords:
KNN, Support Vector Machine, decision Tree, Naive Bayes and Artificial Neural NetworkAbstract
Diabetes is caused by the high blood sugar. Body’s main source of energy is glucose. Our body can produce glucose, but glucose also comes from the various foods we eat. One of the hormone called Insulin is generated by the pancreas to help glucose to move into the cells and to be used for energy later. If anyone is diabetic then body doesn’t make sufficient, or any insulin, or doesn’t usage insulin appropriately. Glucose then remains in the blood and not able to move to cells. Diabetes involves the risk of damage to the eyes, kidneys, nerves, and heart. Early prediction of diabetes can lower the risk of developing diabetes health problems. This paper uses five different techniques from data mining and machine learnings- KNN, Support Vector Machine, decision Tree, Naive Bayes and Artificial Neural Network for the prediction of diabetes. Comparative study based on the performance of these algorithms has been presented. The measures used for the performance analysis of all the five algorithms are Accuracy, Precision, Recall, f1-score and Support. For the experiment purpose the dataset is taken from Mendeley data[1] . It has records of 1000 patients. Result shows that decision tree achieved the best accuracy as compared to the other data mining and machine learning techniques.
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