Diabetes Prediction using Machine Learning Algorithms

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, k...

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Vydáno v:Procedia computer science Ročník 165; s. 292 - 299
Hlavní autoři: Mujumdar, Aishwarya, Vaidehi, V
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 2019
Témata:
Big Data Analytics
Diabetes Mellitus
Healthcare
Machine Learning
Healthcare
Big Data Analytics
Diabetes Mellitus
Machine Learning
ISSN:1877-0509, 1877-0509
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Shrnutí: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 an 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. Classification accuracy is boosted with new dataset compared to existing dataset. Further with imposed a pipeline model for diabetes prediction intended towards improving the accuracy of classification.
ISSN:1877-0509
1877-0509
DOI:10.1016/j.procs.2020.01.047