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Paper Title : Comparative Analysis Of Diabetes Prediction Using Logistic Regression and KNN
ISSN : 2395-1303
Year of Publication : 2021
MLA Style: -Prof.Preeti.B,Aishwarya S Bingeri, Akshata Sajjan ,Muskan Khazi, Nisha M Patil , " Comparative Analysis Of Diabetes Prediction Using Logistic Regression and KNN " Volume 7 - Issue 2 March - April,2021 International Journal of Engineering and Techniques (IJET) ,ISSN:2395-1303 , www.ijetjournal.org
APA Style: -Prof.Preeti.B,Aishwarya S Bingeri, Akshata Sajjan ,Muskan Khazi, Nisha M Patil , " Comparative Analysis Of Diabetes Prediction Using Logistic Regression and KNN " Volume 7 - Issue 2 March - April,2021 International Journal of Engineering and Techniques (IJET) ,ISSN:2395-1303 , www.ijetjournal.org
- Diabetes Mellitus is among critical diseases and lots of people are suffering from this disease. Age, obesity, lack of exercise, hereditary diabetes, living style, bad diet, high blood pressure, etc can cause Diabetes Mellitus. People having diabetes have high risk of diseases like heart disease, kidney disease, stroke, eye problem, nerve damage, etc. Current practice in hospital is to collect required information for diabetes diagnosis through various tests and appropriate treatment is provided based on diagnosis. Big Data Analytics plays a significant role in healthcare industries. Healthcare industries have large volume databases. Using big data analytics one can study huge datasets and find hidden information, hidden patterns to discover knowledge from the data and predict outcomes accordingly. In existing method, the classification and prediction accuracy is not so high. In this paper, we have proposed a diabetes prediction model for better classification of diabetes which includes few external factors responsible for diabetes along with regular factors like Glucose, BMI, Age, Insulin, etc. we are using the KNN and Logistic Regression algorithms to predict the level of diabetes with future risk and compare the results obtained.
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-Diabetes Mellitus, Machine learning , KNN, Logistic Regression, Confusion Matrix, Accuracy Score.