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dc.contributor.authorAnubhav, Adarsh
dc.contributor.authorUpadhyay, Ankit kr.
dc.date.accessioned2023-12-07T04:38:56Z
dc.date.available2023-12-07T04:38:56Z
dc.date.issued2022-05
dc.identifier.urihttp://10.10.11.6/handle/1/12266
dc.description.abstractIn such a fast-booming world of technologies there are diseases that are growing along. With the increasing population around the world. Health service have become one of the most challenging aspects that is greatly affecting people a lot so, our team have decided to build a website using which people across the entire world can get to know the severity of the disease they are suffering from by simple accessing through internet. The proposed method shows promising results both for the distinction of recordings between healthy subjects and patients and for the detection of different disease phases using image processing. It may lead to the easier identification of new disease in patients and the development of home-based monitors for avoiding hospitalizations. It aims at finding significant features by applying machine learning techniques resulting in improving the accuracy in the prediction of disease. The prediction model is introduced with different combinations of features and several known classification techniques. It makes use of artificial Intelligence, machine learning and database management techniques to extract new patterns from large data sets and the knowledge associated with these patterns. By using this technique data can be extracted automatically or semi automatically. The different parameters included in data mining include clustering, forecasting, path analysis and predictive analysis.en_US
dc.language.isoen_USen_US
dc.publisherGALGOTIAS UNIVERSITYen_US
dc.subjectComputer Science, Engineering, MACHINE LEARNING, ML, DISEASE, PREDICTIONen_US
dc.subjectMachine learning, Deep learning, medical image processing, Data Mining, clinical predictions, artificial intelligence, clustering, predictive analysis, forecastingen_US
dc.titleDISEASE PREDICTION USING MACHINE LEARNINGen_US
dc.typeTechnical Reporten_US


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