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Diabetes Prediction System using Machine Learning

Show simple item record Thenabadu, TK Ilmini, WMKS 2020-12-31T20:01:10Z 2020-12-31T20:01:10Z 2020
dc.description.abstract Abstract: Diabetes is a deadly chronic disease which affects entire body system harmfully. Millions of people are affected by this disease and a considerable number of patients die every year because of its side effects. A diabetic patient suffers from a high level of blood sugar in the body. Undiagnosed diabetes may cause the nerve and kidney damage, heart and blood vessel disease, slow healing of wounds, hearing impairment and several skin diseases. Early detection of diabetes is very essential to have a healthy life. The recent development of Machine Learning approaches solves this kind of critical problems. The main objective of this study is to present a Machine Learning based solution (Artificial Neural Network) to solve the above problem. And also, the technologies and approaches used in previous researches to predict diabetes have been reviewed with their accuracy levels. All the previous studies have used “Pima Indian Diabetes Dataset” (PIDD) as the dataset but this research is based on a newly collected dataset. The overall development process can be categorized into four major development phases namely data collection and preprocessing, statistical analysis, development of machine learning model and development of front-end. Artificial Neural Network model has been developed and deployed while the model provides more than 92% accuracy on the sample testing dataset. en_US
dc.language.iso en en_US
dc.subject Diabetes en_US
dc.subject Machine Learning en_US
dc.subject Artificial Intelligence en_US
dc.subject Artificial Neural Network en_US
dc.subject Android en_US
dc.subject Tensorflow en_US
dc.subject Firebase en_US
dc.title Diabetes Prediction System using Machine Learning en_US
dc.type Article Full Text en_US
dc.identifier.journal 13th International Research Conference General Sir John Kotelawala Defence University en_US
dc.identifier.pgnos 79-86 en_US

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