Please use this identifier to cite or link to this item: http://ir.juit.ac.in:8080/jspui/jspui/handle/123456789/6990
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dc.contributor.authorGarg, Vedika-
dc.contributor.authorGupta, Pradeep Kumar [Guided by]-
dc.date.accessioned2022-09-29T06:24:49Z-
dc.date.available2022-09-29T06:24:49Z-
dc.date.issued2019-
dc.identifier.urihttp://ir.juit.ac.in:8080/jspui/jspui/handle/123456789/6990-
dc.description.abstractDiabetes has affected over 246 million people worldwide with a majority of them being women. According to the WHO report, by 2025 this number is expected to rise to over 380 million. The disease has been named the fifth deadliest disease in the United States with no imminent cure in sight. With the rise of information technology and its continued advent into the medical and healthcare sector, the cases of diabetes, as well as their symptoms, are well documented. Our report focuses on quicker and efficient techniques for the diagnosis of diabetes disease. Traditional techniques like Random Forest Classifier had a few limitations in predicting the outcome of the disease. So we try to implement more advanced machine learning algorithms in our research like Naive Bayes, k-nearest neighbor and Decision Trees which give better predictions and increased accuracy. Along with these algorithms, we will be implementing many other algorithms too and be doing a comprehensive and a comparative study on these algorithms to get the best of all. This study can further help in determining which algorithms to use.en_US
dc.language.isoenen_US
dc.publisherJaypee University of Information Technology, Solan, H.P.en_US
dc.subjectDiabetesen_US
dc.subjectMachine learning algorithmsen_US
dc.subjectNeural networken_US
dc.subjectBayesian networksen_US
dc.titleImplementation of Machine Learning Algorithms for Analyzing Diabetes Diseaseen_US
dc.typeProject Reporten_US
Appears in Collections:B.Tech. Project Reports

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