SUPPORT VECTOR MACHINE BASED DISEASE DIAGNOSTIC ASSISTANT

Abstract
There has been a huge growth both in data and computing technology which has made it easier for the development of artificial intelligent systems that are capable of learning from this data and make medical diagnosis on their own. In this paper, Support Vector Machines (SVM) are used in implementing a multi-disease diagnostic assistant application that is able to make predictions, early detections and instant diagnosis of various illness based on given patient data. The application is implemented in an easy to use graphical user interface and contains pretrained SVM models of predicting several diseases. A medical staff creates a new patient entry and enters or uploads a patient’s required diagnostic data, once done the application gives multiple diagnosis based on the diagnostic data. In case the application makes a wrong diagnosis, it can learn from its mistake through correction from the medical staff, enabling future similar diagnosis to be correct.

Authors
Samuel Ndirangu, Davies Segera
Rajasthan Technical University, India

Keywords
Bayesian Optimization, Kernel Function, Sequential Feature Selection, Support Vector Machine
Published By :
ICTACT
Published In :
ICTACT Journal on Soft Computing
( Volume: 9 , Issue: 4 )
Date of Publication :
July 2019

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