Prediction of Diabetes using Machine Learning |
Author(s): |
| Ankita Kulkarni , Trinity College of Engineering and Research |
Keywords: |
| Naïve Bayes, SVC, Random Forest Classifier, Machine Learning, Data-Preprocessing |
Abstract |
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Classifying a dataset has always been a computational method. This paper represents the classification of female Pima Indians patients who were diagnosed for diabetes type-II. It comprises of 768 records of medical details of the patients. It provides measurements of Pregnancies, Glucose, Blood Pressure, Skin Thickness, Insulin, BMI, Diabetes Pedigree Function, Age and finally the outcome in binary if the patient has encountered diabetes or not. We train the system using various classifiers to classify patients into positive and negative classes and then differentiate the classification in accuracy with and without data pre-processing. |
Other Details |
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Paper ID: IJSRDV6I70108 Published in: Volume : 6, Issue : 7 Publication Date: 01/10/2018 Page(s): 327-330 |
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