Please use this identifier to cite or link to this item:
Full metadata record
DC FieldValueLanguage
dc.contributor.authorMittal, Archit-
dc.contributor.authorSaxena, Pulkit-
dc.contributor.authorSingh, Yugander Kishan-
dc.contributor.authorVirmani, Jitendra [Guided by]-
dc.description.abstractThe diagnosis of diseases in most cases depends on a complex combination of clinical and pathological data; this complexity leads to excessive medical costs affecting the cost of medical care. If we look at statistics from WHO, one third of population is suffering from either diabetes or heart disease. Among all diseases heart related disease is found to be the leading cause of death in both males and females and leading in case of female.Computation techniques are often applied for understanding biological phenomena from medical data. For example the discovery of biomarkers in heart disease is one of the key contributions using computational techniques. This process involves the development of predictive model and integration of different types of data and knowledge for diagnostic purposes. For developing computational techniques related to diseases like heart or diabetes data mining has played an important role in research. To find the hidden medical information from different expression between the healthy and diseased individual in the existed clinical data is a noticeable and powerful approach in study of disease classification. Statistics and machine learning are two main approaches which have been applied to predict the status of disease based on the expression of clinical data.en_US
dc.publisherJaypee University of Information Technology, Solan, H.P.en_US
dc.subjectMachine learningen_US
dc.subjectCAD systemen_US
dc.subjectHybrid CAD systemen_US
dc.titleApplication of Machine Learning Algorithms on Benchmark Medical Datasetsen_US
dc.typeProject Reporten_US
Appears in Collections:B.Tech. Project Reports

Files in This Item:
File Description SizeFormat 
Application of Machine Learning Algorithms on Benchmark Medical Datasets.pdf851.68 kBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.