Disease prediction using SVM and Machine Learning
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Date
2018-09-05T11:26:53Z
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Abstract
Disease prediction is of critical importance with respect to advancements in health-car e
as well as technology. With the advent of big data and analytics, it has become feasible to
develop methods to diagnose and predict diseases in cost effective ways. However, the
accuracy of analysis is decreased when the nature of medical data is incomplete.
Furthermore, different regions exhibit distinctive characteristics of certain regional
diseases, which have the probability to weaken the prediction of disease outbreaks. Thus,
there lies a need to establish effective methods by which the predicted values can be
separated for successful prediction. The predicted values are such that 0 denotes absence
and 1 denotes presence, which is again used to check model performance. An algorithm
in python is used to conduct this classification.
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Divya Balaji, Deekshita S, S Harshita, 1NH14IS034, 1NH14IS029, 1NH14IS099