Please use this identifier to cite or link to this item: http://hdl.handle.net/11718/14033
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dc.contributor.authorJain, Amar
dc.contributor.authorSinghal, Shivang
dc.contributor.authorPagidimarri, Venkatesh
dc.contributor.authorKasivajjala, Vamsi C.
dc.contributor.authorDubey, Ayush
dc.contributor.authorSarkar, Suvomoy
dc.date.accessioned2015-07-08T06:39:59Z
dc.date.available2015-07-08T06:39:59Z
dc.date.issued2015
dc.identifier.citationJain, A., Singhal, S., Pagidimarri, V., Kasivajjala, V. C., Dubey, A., & Sarkar, S.. (2015). Predicting Risk of Diabetes in Non-Diabetic Population. 4th IIMA International Conference on Advanced Data Analysis, Business Analytics and Intelligence. Indian Institute of Management, Ahmedabaden_US
dc.identifier.urihttp://hdl.handle.net/11718/14033
dc.description.abstractDiabetes Mellitus is a chronic debilitating disease affecting a major population of the developed and developing countries. The occurrence of type 2 diabetes mellitus (T2DM) is rising rapidly among middle-aged American adults. It has been estimated that the prevalence of diabetes in the United States increased from 7.3% in 1993 to 7.9% by the year 2000, and greater frequencies are forecast for the future Prediction of chronic conditions like DM that have a definable onset can help to guide interventions and health policy development. Prediction of future incidence of this disease will enable adequate fund allocation for delivery of care to be planned. This white paper discusses the approach and statistical models used to predict diabetes mellitus in a population with unknown status for diabetes. The prediction is for at present and at three months’ time frame allowing a practitioner to pick up patients at risk of acquiring diabetes. The problem is modeled as supervised classification problem, training data consisted of all the labelled patients and model accuracy is validated on test data set. Multiple models are built with proper tuning, and their performances are compared. Support Vector Machine and Random Forest have better accuracy compared to other models.en_US
dc.language.isoenen_US
dc.publisherIndian Institute of Management, Ahmedabaden_US
dc.relation.ispartofseriesIC 15;033
dc.subjectSupport Vector Machineen
dc.subjectRandom Foresten
dc.subjectDiabetes Mellitusen
dc.subjectSupervised- Classificationen
dc.titlePredicting Risk of Diabetes in Non-Diabetic Populationen_US
dc.typeArticleen_US
Appears in Collections:4th IIMA International Conference on Advanced Data Analysis, Business Analytics and Intelligence

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