ICTACT Journal on Soft Computing

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Support Vector Machine based Disease Diagnostic Assistant


Affiliations
1 Department of Electrical and Information Engineering, University of Nairobi, Kenya
     

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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.

Keywords

Bayesian Optimization, Kernel Function, Sequential Feature Selection, Support Vector Machine.
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  • Support Vector Machine based Disease Diagnostic Assistant

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Authors

Samuel Ndirangu
Department of Electrical and Information Engineering, University of Nairobi, Kenya
Davies Segera
Department of Electrical and Information Engineering, University of Nairobi, Kenya

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.

Keywords


Bayesian Optimization, Kernel Function, Sequential Feature Selection, Support Vector Machine.

References