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Computer-Aided Medical Diagnosis Using Bayesian Classifier - Decision Support System for Medical Diagnosis

Authors:

N.T. De Silva ,

School of Computing, National School of Business Management, LK
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D.J. Jayamanne

School of Computing, National School of Business Management, LK
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Abstract

This study employs a Bayesian framework to construct a Web-based decision support system for medical diagnosis. The purpose is to help users (patients and physicians) with issues pertinent to medical diagnosis decisions and to detect diseases with highest probability through the Bayesian framework. Users could perform a more accurate diagnosis with the prior/conditional probabilities obtained from selected data sets and compute the posterior probability using the Bayes theorem. The proposed system identifies diseases by analyzing symptoms or by analyzing medical test results. Currently the system detects different types of diseases that people suffer in their day-to-day lives (general diseases) with an average detection accuracy of 92.56%. System also detects complex diseases (e.g.: heart disease - 83.67%, breast cancer - 80.98%, liver disorders - 79.43%, lung cancer - 71.00%, primary tumor - 78.02%, etc.) based on the analysis of the medical test results. The proposed system enhances the quality, accuracy and efficiency of decisions in medical diagnosis since the use of Bayesian theorem allows this system to offer more accurate platform than the conventional systems. Other than that this web-based system provides value-added services in conjunction with CAD system, such as; e-Chat & e-Channeling. More importantly, the targeted user group will be able to access the system as a software element freely and quickly. In this way the goal of this study – which is to provide a web-based medical diagnosis system is effectively achieved.
How to Cite: De Silva, N.T. & Jayamanne, D.J., (2017). Computer-Aided Medical Diagnosis Using Bayesian Classifier - Decision Support System for Medical Diagnosis. International Journal of Multidisciplinary Studies. 3(2), pp.91–97. DOI: http://doi.org/10.4038/ijms.v3i2.11
Published on 28 Jan 2017.
Peer Reviewed

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